Botany 2021
Setup
Install
This workshop was made using R 3.6.2, we suggest you update your R. Prior to starting this workshop, you should have had the following packages installed.
## Make a list of packages
list_of_packages <- c("dplyr",
"tidyr",
"plyr",
"spocc",
"ridigbio",
"tibble",
"tidyverse",
"rbison",
"CoordinateCleaner",
"lubridate",
"ggplot2",
"gtools",
"raster",
"sp",
"spatstat",
"spThin",
"fields",
"ggspatial",
"rgdal",
"rangeBuilder",
"sf",
"dismo",
"devtools",
"ENMeval",
"caret",
"usdm",
"stringr",
"factoextra",
"FactoMineR",
"multcompView",
"ggsci",
"gridExtra",
"ecospat",
"rJava",
"viridis",
"ENMTools",
"ape",
"RStoolbox",
"hypervolume",
"phytools",
"picante")
## If you do not have these packages already installed, install them by executing the code below:
new.packages <- list_of_packages[!(list_of_packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages)
## Checking version of packages
### Compare to version demo was made with
versiondf <- read.csv("data/setup/setup.csv", stringsAsFactors = FALSE)
current_version <- as.character(do.call(c, lapply(list_of_packages, packageVersion)))
versiondf$current_version <- current_version
updatelist <- versiondf[which(versiondf$version != versiondf$current_version), ]
### Update packages with old versions
lapply(as.character(updatelist$packages), install.packages, ask = FALSE)
## Make sure all packages load
lapply(list_of_packages, require, character.only = TRUE)# Install packages not in CRAN
library(devtools)
install_github('johnbaums/rmaxent')
install_github("marlonecobos/kuenm")What are these packages?
tidyverse is a collection of R packages that are used for “everyday data analysis” including ggplot2, dplyr, tidyr, readr, purr, tibble, stringr, forcats. We use dplyr and ggplot2 throughout many of the R scripts.
ridigbio is an R package that queries the iDigBio API.
spocc queries data from GBIF, USGS’ Biodiversity Information Serving Our Nation (BISON), iNaturalist, Berkeley Ecoinformatics Engine, eBird, iDigBio, VertNet, Ocean Biogeographic Information System (OBIS), and Atlas of Living Australia (ALA).
RCurl and rjson are used to query an application programming interface (API), or an online database.
Geospatial packages include sp, raster, maptools, maps, rgdal, RStoolbox, and mapproj.
ggplot2 is used to plot and visualize data. ggbiplot is an add on to ggplot2.
ENMTools, ENMeval, and dismo are specific to ecological niche modeling.
hypervolume and caret have functions related to statistics.
phytools and picante are related to phylogenetics.
factoextra and FactoMiner are used for statistics related to a principal components analysis.
Troubleshooting
- For spatstat make sure xcode is installed on OS.
system("xcode-select --install")spThin and fields do not compile from source!
If you are still having issues installing ggbiplot, you may have to uninstall the package ‘backports’ and reset R.
This exercise requires the package ENMTools. This package requires Java (and the Oracle Java JDK) and that the maxent.jar file is in the folder that contains the dismo package (this package is a dependency of ENMTools). To find out where to put the jar file, run the command:
system.file("java", package="dismo")Once you have your java and maxent file set, you are ready to go! If you have more issues, find some help here
To download the jar file to the right directory —
devtools::install_github("johnbaums/rmaxent")
rmaxent::get_maxent(version = "latest", quiet = FALSE)The previous step may mask rgdal! Make sure to reload it!
For rJava, the newest package requires R >= 3.6.0
dismo only requires rJava >= 0.9-7, download 0.9.7 here
If you are experiencing errors with rJava:
## Remove rJava
remove.packages(rJava)
## Update your R Java configuration
system("sudo R CMD javareconf")
## Restart R and check to see if the path matches
Sys.getenv("DYLD_FALLBACK_LIBRARY_PATH")
## Reinstall rJava
install.packages("rJava")
library(rJava)If you are still having issues with rJava - check to see if you are using Java 12 (this should be included in the error message):
system("java -version")The initial release of java 12 does not work - install an old version instead. To install an old version, navigate to the Oracle website or use homebrew.
system("brew cask install homebrew/cask-versions/java11")Restart R and redo the previous steps. Required: uninstall the newer versions of java prior to repeating and restart R after.
## Uninstall Java 12, then repeat the following
## Remove rJava
remove.packages(rJava)
## Update your R Java configuration
system("sudo R CMD javareconf")
## Restart R and check to see if the path matches
Sys.getenv("DYLD_FALLBACK_LIBRARY_PATH")
## Reinstall rJava
install.packages("rJava")
library(rJava)More debugging: Check out jerrybreak’s comment from 12/30/2021.
If you have additional issues, please let us know!
Download Occurrence Data
Modified and created by ML Gaynor.
Load packages
library(dplyr)
library(tidyr)
library(plyr)
library(spocc)
library(ridigbio)
library(tibble)
library(rbison)Load functions
This is a function I created with Natalie Patten. It will be part of her R package gatoRs (Geographic And Taxonomic Occurrence R-based Scrubbing).
source("functions/gators.R")Data download from iDigBio
Search for the species Galax urceolata.
iDigBio_GU <- idig_search_records(rq=list(scientificname="Galax urceolata"))Search for the family Diapensiaceae.
iDigBio_GU_family <- idig_search_records(rq=list(family="Diapensiaceae"), limit=1000)What if you want to read in all the points for a family within an extent?
Hint: Use the iDigBio portal to determine the bounding box for your region of interest.
The bounding box delimits the geographic extent.
rq_input <- list("scientificname"=list("type"="exists"),
"family"="Diapensiaceae",
geopoint=list(
type="geo_bounding_box",
top_left=list(lon = -98.16, lat = 48.92),
bottom_right=list(lon = -64.02, lat = 23.06)
)
)Search using the input you just made
iDigBio_GU_family_USA <- idig_search_records(rq_input, limit=1000)Save as csv files
write.csv(iDigBio_GU, "data/download/iDigBio_GU_20210614.csv",
row.names = FALSE)
write.csv(iDigBio_GU_family, "data/download/iDigBio_GU_family_20210614.csv",
row.names = FALSE)Data download using spocc_combined
Make synonym lists
Shortia_galacifolia <- c("Shortia galacifolia", "Sherwoodia galacifolia")
Galax_urceolata <- c("Galax urceolata", "Galax aphylla")
Pyxidanthera_barbulata <- c("Pyxidanthera barbulata","Pyxidanthera barbulata var. barbulata")
Pyxidanthera_brevifolia <- c("Pyxidanthera brevifolia", "Pyxidanthera barbulata var. brevifolia")Use the spocc_combine function
This function downloads records for all names listed from iDigBio, GBIF, and BISON. It keeps specific columns from each database.
spocc_combine(Shortia_galacifolia, "data/download/raw/Shortia_galacifolia_raw_20210614.csv")
spocc_combine(Galax_urceolata, "data/download/raw/Galax_urceolata_raw_20210614.csv")
spocc_combine(Pyxidanthera_barbulata, "data/download/raw/Pyxidanthera_barbulata_raw_20210614.csv")
spocc_combine(Pyxidanthera_brevifolia, "data/download/raw/Pyxidanthera_brevifolia_raw_20210614.csv")Occurrence Data Cleaning
Modified and created by ML Gaynor.
Load Packages
library(dplyr)
library(tidyr)
library(raster)
library(sp)
library(spatstat)
library(spThin)
library(fields)
library(lubridate)
library(CoordinateCleaner)
library(ggplot2)
library(ggspatial)Load functions
This is a function I created with Natalie Patten. It will be part of her R package gatoRs (Geographic And Taxonomic Occurrence R-based Scrubbing).
source("functions/gators.R")Read in downloaded data frame
rawdf <- read.csv("data/download/raw/Shortia_galacifolia_raw_20210614.csv")Cleaning
Inspect the data frame
What columns are included?
names(rawdf)## [1] "name" "basis"
## [3] "date" "institutionID"
## [5] "collectionCode" "collectionID"
## [7] "country" "county"
## [9] "state" "locality"
## [11] "Latitude" "Longitude"
## [13] "ID" "coordinateUncertaintyInMeters"
## [15] "informationWithheld" "habitat"
## [17] "prov" "spocc.latitude"
## [19] "spocc.longitude" "spocc.prov"
## [21] "spocc.date" "spocc.name"How many observations do we start with?
nrow(rawdf)## [1] 7911. Resolve taxon names
Inspect scientific names included in the raw df.
unique(rawdf$name)## [1] shortia galacifolia sherwoodia galacifolia
## [3] Shortia galacifolia Torr. & A.Gray
## 3 Levels: sherwoodia galacifolia ... Shortia galacifolia Torr. & A.GrayCreate a list of accepted names based on the name column in your data frame
search <- c("Shortia galacifolia", "Sherwoodia galacifolia")Filter to only include accepted name:
df <- filter_fix_names(rawdf, listofsynonyms = search, acceptedname = "Shortia galacifolia")## [1] "List of synonyms:"
## [1] "Shortia galacifolia" "Sherwoodia galacifolia"
## [1] "Shortia galacifolia|Sherwoodia galacifolia"
## [1] "Looking at unique names in raw df"
## [1] "shortia galacifolia" "sherwoodia galacifolia"
## [3] "Shortia galacifolia Torr. & A.Gray"
## [1] "Looking at unique names in cleaned df"
## [1] "shortia galacifolia" "sherwoodia galacifolia"
## [3] "Shortia galacifolia Torr. & A.Gray"How many observations do we have now?
nrow(df)## [1] 7912. Decrease number of columns
Merge the two locality columns
df$Latitude <- dplyr::coalesce(df$Latitude, df$spocc.latitude)
df$Longitude <- dplyr::coalesce(df$Longitude, df$spocc.longitude)If spocc didn’t download any lat/longs and there are 0 values in the spocc.latitude or spocc.longitude columns, skip this step
The two columns could have different classes, if so, find the fix here
Merge the two date columns
df$date <- dplyr::coalesce(df$date, df$spocc.date)Subset the columns
df <- df %>%
dplyr::select(ID = ID,
name = new_name,
basis = basis,
coordinateUncertaintyInMeters = coordinateUncertaintyInMeters,
informationWithheld = informationWithheld,
lat = Latitude,
long = Longitude,
date = date)3. Clean localities
Filtering out NA’s
df <- df %>%
filter(!is.na(long)) %>%
filter(!is.na(lat))How many observations do we have now?
nrow(df)## [1] 202Precision
Round to two decimal places
df$lat <- round(df$lat, digits = 2)
df$long <- round(df$long, digits = 2)Remove unlikely points
Remove points at 0.00, 0.00
df <- df %>%
filter(long != 0.00) %>%
filter(lat != 0.00)Remove coordinates in cultivated zones, botanical gardens, and outside our desired range
df <- CoordinateCleaner::cc_inst(df,
lon = "long",
lat = "lat",
species = "name")## Testing biodiversity institutions## Removed 0 records.Next, we look for geographic outliers and remove outliers.
df <- CoordinateCleaner::cc_outl(df,
lon = "long",
lat = "lat",
species = "name")## Testing geographic outliers## Removed 40 records.How many observations do we have now?
nrow(df)## [1] 1614. Remove Duplicates
Fix dates
Parse dates into the same format
df$date <- lubridate::ymd(df$date)Separate date into year, month, day
df <- df %>%
mutate(year = lubridate::year(date),
month = lubridate::month(date),
day = lubridate::day(date))Remove rows with identical lat, long, year, month, and day
df <- distinct(df, lat, long, year, month, day, .keep_all = TRUE)How many observations do we have now?
nrow(df)## [1] 1575. Spatial Correction
Maxent will only retain one point per pixel. To make the ecological niche analysis comparable, we will retain only one point per pixel.
One point per pixel
Read in raster file
bio1 <- raster("data/climate_processing/bioclim/bio_1.tif")Set resolution
rasterResolution <- max(res(bio1))Filter
Remove a point which nearest neighbor distance is smaller than the resolution size (aka remove one point in a pair that occurs within one pixel)
while(min(nndist(df[,6:7])) < rasterResolution){
nnD <- nndist(df[,6:7])
df <- df[-(which(min(nnD) == nnD) [1]), ]
}How many observations do we have now?
nrow(df)## [1] 151Spatial thinning
Reduce the effects of sampling bias using randomization approach.
Calculate minimum nearest neighbor distance in km
nnDm <- rdist.earth(as.matrix(data.frame(lon = df$long, lat = df$lat)), miles = FALSE, R = NULL)
nnDmin <- do.call(rbind, lapply(1:5, function(i) sort(nnDm[,i])[2]))
min(nnDmin)## [1] 1.43369Identify points to keep using spThin
keep <- spThin::thin(loc.data = df,
verbose = FALSE,
long.col = "long",
lat.col = "lat",
spec.col = "name",
thin.par = 0.002, # Studies found 2m distance was enough to collect unique genets
reps = 1,
locs.thinned.list.return = TRUE,
write.files = FALSE)[[1]]Filter df to only include those lat/long that have been kept using spThin
df <- df %>%
filter((lat %in% keep$Latitude +
long %in% keep$Longitude) == 2)
nrow(df)## [1] 1516. Plot Cleaned Records
Set basemap
USA <- borders(database = "usa", colour = "gray50", fill = "gray50")
state <- borders(database = "state", colour = "black", fill = NA)Plot
Here we are using ggplot2 and ggspatial to plot our occurrence records. We are using two basemaps, USA and state. The geom_point function adds our points based on long and lat, and colors them blue. We set the coordinate visualization space with coord_sf. We fixed the x and y labels. Finally, we added a scale and north arrow.
simple_map <- ggplot() +
USA +
state +
geom_point(df,
mapping = aes(x = long, y = lat),
col = "blue") +
coord_sf(xlim = c(min(df$long) - 3, max(df$long) + 3),
ylim = c(min(df$lat) - 3, max(df$lat) + 3)) +
xlab("Longitude") +
ylab("Latitude") +
annotation_scale() +
annotation_north_arrow(height = unit(1, "cm"),
width = unit(1, "cm"),
location = "tl")
simple_map## Using plotunit = 'm'
7. Save Cleaned.csv
write.csv(df, "data/cleaning_demo/Shortia_galacifolia_20210618-cleaned.csv", row.names = FALSE)8. Make maxent ready
Read in all cleaned files
alldf <- list.files("data/cleaning_demo/raw/", full.names = TRUE,
recursive = FALSE, include.dirs = FALSE, pattern = "*.csv")
alldf <- lapply(alldf, read.csv)
alldf <- do.call(rbind, alldf)Select needed columns
alldf <- alldf %>%
dplyr::select(name, lat, long)Save Maxent.csv
write.csv(alldf, "data/cleaning_demo/maxent_ready/diapensiaceae_maxentready_20210618.csv", row.names = FALSE)Climate Processing
Climate layer processing.
Modified and created by ML Gaynor.
Based on code by Mike Belitz (mbelitz/Odo_SDM_Rproj).
Load Packages
library(raster)
library(gtools)
library(dplyr)
library(rgdal)
library(sp)
library(rangeBuilder)
library(sf)
library(caret)
library(usdm)
library(dismo)
library(stringr)Load functions
Based on code by Mike Belitz (mbelitz/Odo_SDM_Rproj).
source("functions/VIFLayerSelect.R")Load bioclim layers
biolist <- list.files("data/climate_processing/bioclim/", pattern = "*.tif", full.names = TRUE)Order list using gtools.
biolist <- mixedsort(sort(biolist))Load rasters as a stack.
biostack <- raster::stack(biolist)Load occurrence records
And fix the name column class and make sure it is a character.
alldf <- read.csv("data/cleaning_demo/maxent_ready/diapensiaceae_maxentready_20210625.csv")
alldf$name <- as.character(alldf$name)Present Layers - all
Here we will make the projection layers, or the layers which include shared space. First, we have to define the accessible space.
Make into a spatial point data frame
alldfsp <- alldf
coordinates(alldfsp) <- ~ long + lat
proj4string(alldfsp) <- CRS("+proj=longlat +datum=WGS84")Create alpha hull
hull <- getDynamicAlphaHull(x = alldfsp@coords,
fraction = 1, # min. fraction of records we want included
partCount = 1, # number of polygons
initialAlpha = 20, # initial alpha size, 20m
clipToCoast = "terrestrial",
proj = "+proj=longlat +datum=WGS84")Visualize
plot(hull[[1]], col=transparentColor('gray50', 0.5), border = NA)
points(x = alldf$long, y = alldf$lat, cex = 0.5, pch = 3)
Add buffer to hull
Calculate buffer size
Here we take the 80th quantile of the max distance between points
buffDist <- quantile(x = (apply(spDists(alldfspTrans), 2,
FUN = function(x) sort(x)[2])),
probs = 0.80, na.rm = TRUE) Buffer the hull
buffer_m <- buffer(x = hullTrans, width = buffDist, dissolve = TRUE)
buffer <- spTransform(buffer_m, "+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0")Visualize
plot(buffer, col=transparentColor('gray50', 0.5), border = NA)
points(x = alldf$long, y = alldf$lat, cex = 0.5, pch = 3)
Mask and crop bioclim layers
path <- "data/climate_processing/all/"
end <- ".asc"
for(i in 1:length(biolist)){
# Subset raster layer
rast <- biostack[[i]]
# Setup file names
name <- names(rast)
out <- paste0(path, name)
outfile <- paste0(out, end)
# Crop and mask
c <- crop(rast, extent(buffer))
c <- mask(c, buffer)
# Write raster
writeRaster(c, outfile, format = "ascii", overwrite = TRUE)
}Select layers for MaxEnt
We only want to include layers that are not highly correlated. To assess which layers we will include, we can look at the pearson correlation coefficient among layers.
Stack all layers
clippedlist <- list.files("data/climate_processing/all/", pattern = "*.asc", full.names = TRUE)
clippedlist <- mixedsort(sort(clippedlist))
clippedstack <- raster::stack(clippedlist)Then calculate the correlation coefficient
corr <- layerStats(clippedstack, 'pearson', na.rm=TRUE)Isolate only the pearson correlation coefficient and take absolute value.
c <- abs(corr$`pearson correlation coefficient`)Write file and view in excel
write.csv(c, "data/climate_processing/correlationBioclim.csv", row.names = FALSE)Highly correlated layers (> |0.80|) can impact the statistical significance of the niche models and therefore must be removed.
Randomly select variables to remove
envtCor <- mixedsort(sort(findCorrelation(c, cutoff = 0.80, names = TRUE, exact = TRUE)))
envtCor## [1] "bio_1" "bio_3" "bio_4" "bio_6" "bio_10" "bio_11" "bio_12" "bio_13"
## [9] "bio_16" "bio_17" "bio_19"Variable inflation factor (VIF)
VIF can detect for multicollinearity in a set of multiple regression variables. Run a simple maxent model for every species and calculate the average permutation contribution.
Run preliminary MaxEnt models
Loop through each species and save permutation importance in list.
Warning: This will print warnings even when it works fine.
set.seed(195)
m <- c()
for(i in 1:length(unique(alldf$name))){
species <- unique(alldf$name)[i]
spp_df <- alldf %>%
dplyr::filter(name == species)
coordinates(spp_df) <- ~ long + lat
model <- maxent(x = clippedstack, p = coordinates(spp_df),
progress = "text", silent = FALSE)
m[[i]] <- vimportance(model)
}Bind the dataframes
mc <- do.call(rbind, m)Calculate the mean and rename columns
mc_average <- aggregate(mc[, 2], list(mc$Variables), mean)
mc_average <- mc_average %>%
dplyr::select(Variables = Group.1, permutation.importance = x)
mc1 <- mc_averageSelect Layers
Use VIF and the MaxEnt permutation importance to select the best variables for your model. Note, this leads to different layers when the models are rerun without setting seed due to permutations being random.
selectedlayers <- VIF_layerselect(clippedstack, mc_average)
mixedsort(sort(names(selectedlayers)))Correct set for workshop purposes
Since this can vary per system (despite setting seed), we added this line to keep our files consistent for the workshop
sl <- c("bio_3", "bio_7", "bio_8", "bio_9", "bio_14", "bio_15", "bio_18", "elev")
selectedlayers <- raster::subset(clippedstack, sl)Copy selected layers
Move selected layers to “Present_Layers/all/” for use in MaxEnt later.
for(i in 1:length(names(selectedlayers))){
name <- names(selectedlayers)[i]
from <- paste0("data/climate_processing/all/", name, ".asc")
to <- paste0("data/climate_processing/PresentLayers/all/", name, ".asc")
file.copy(from, to,
overwrite = TRUE, recursive = FALSE,
copy.mode = TRUE)
}Create Species Training Layers
for(i in 1:length(unique(alldf$name))){
species <- unique(alldf$name)[i]
# Subset species from data frame
spp_df <- alldf %>%
dplyr::filter(name == species)
# Make spatial
coordinates(spp_df) <- ~ long + lat
proj4string(spp_df) <- CRS("+proj=longlat +datum=WGS84")
## Create alpha hull
sphull <- getDynamicAlphaHull(x = spp_df@coords,
fraction = 1, # min. fraction of records we want included
partCount = 1, # number of polygons
initialAlpha = 20, # initial alpha size, 20m
clipToCoast = "terrestrial",
proj = "+proj=longlat +datum=WGS84")
### Transform into CRS related to meters
sphullTrans <- spTransform(sphull[[1]], "+proj=cea +lat_ts=0 +lon_0=0")
spp_dfTrans <- spTransform(spp_df, "+proj=cea +lat_ts=0 +lon_0")
### Calculate buffer size
#### Here we take the 80th quantile of the max distance between points
spbuffDist <- quantile(x = (apply(spDists(spp_dfTrans), 2, FUN = function(x) sort(x)[2])),
probs = 0.80, na.rm = TRUE)
### Buffer the hull
spbuffer_m <- buffer(x = sphullTrans, width = spbuffDist, dissolve = TRUE)
spbuffer <- spTransform(spbuffer_m,
"+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0")
### Crop and Mask
spec <- gsub(" ", "_", species)
path <- paste0("data/climate_processing/PresentLayers/", spec,"/")
end <- ".asc"
for(j in 1:length(names(selectedlayers))){
# Subset raster layer
rast <- selectedlayers[[j]]
# Setup file names
name <- names(rast)
out <- paste0(path, name)
outfile <- paste0(out, end)
# Crop and mask
c <- crop(rast, extent(buffer))
c <- mask(c, buffer)
# Write raster
writeRaster(c, outfile, format = "ascii", overwrite = TRUE)
}
}Point Based
Ecological analysis using points. This is based off scripts created by Hannah Owens and James Watling, and Anthony Melton.
Modified and created by ML Gaynor.
Load Packages
library(raster)
library(dplyr)
library(tidyr)
library(gtools)
library(factoextra)
library(FactoMineR)
library(multcompView)
library(ggsci)
library(gridExtra)
library(ggplot2)
library(ecospat)
library(dismo)Load data file
alldf <- read.csv("data/cleaning_demo/maxent_ready/diapensiaceae_maxentready_20210625.csv")Raster layers
list <- list.files("data/climate_processing/PresentLayers/all", full.names = TRUE, recursive = FALSE)
list <- mixedsort(sort(list))
envtStack <- stack(list)ENM - Realized Niche
Extract value for each point
For each occurence record, extract the value for each bioclim variables using the package raster.
ptExtracted <- raster::extract(envtStack, alldf[3:2])Convert to data frame
ptExtracteddf <- as.data.frame(ptExtracted)Add species name
ptExtracteddf <- ptExtracteddf %>%
dplyr::mutate(name = as.character(alldf$name), x = alldf$long, y = alldf$lat)Drop any NA
ptExtracteddf <- ptExtracteddf %>%
tidyr::drop_na(bio_3, bio_7, bio_8, bio_9, bio_14, bio_15, bio_18, elev)PCA
Create two dataframes.
data.bioclim <- ptExtracteddf[, 1:8]
data.species <- ptExtracteddf[, 9]Using only the bioclim columns to run the principal components analysis.
data.pca <- prcomp(data.bioclim, scale. = TRUE) Understanding the PCA - Optional
When you use the command prcomp your loading variables show up as rotational variables. Thanks to a really great answer on stack overflow you can even convert the rotational variable to show the relative contribution.
loadings <- data.pca$rotation
summary(loadings)## PC1 PC2 PC3 PC4
## Min. :-0.4467 Min. :-0.45110 Min. :-0.82811 Min. :-0.30022
## 1st Qu.:-0.3468 1st Qu.:-0.17283 1st Qu.:-0.23635 1st Qu.:-0.16254
## Median :-0.3119 Median : 0.13346 Median : 0.10057 Median : 0.01297
## Mean :-0.1722 Mean : 0.07061 Mean :-0.05581 Mean : 0.09998
## 3rd Qu.:-0.1394 3rd Qu.: 0.29849 3rd Qu.: 0.15316 3rd Qu.: 0.33112
## Max. : 0.4699 Max. : 0.51779 Max. : 0.33317 Max. : 0.76997
## PC5 PC6 PC7 PC8
## Min. :-0.54202 Min. :-0.793450 Min. :-0.60781 Min. :-0.65942
## 1st Qu.:-0.15564 1st Qu.:-0.228231 1st Qu.:-0.40173 1st Qu.:-0.18065
## Median : 0.12133 Median :-0.077790 Median :-0.24135 Median :-0.07437
## Mean : 0.09021 Mean :-0.145197 Mean :-0.20110 Mean :-0.08703
## 3rd Qu.: 0.40562 3rd Qu.:-0.005815 3rd Qu.:-0.03176 3rd Qu.: 0.02402
## Max. : 0.48773 Max. : 0.368720 Max. : 0.33905 Max. : 0.57647There are two options to convert the loading to show the relative contribution, they both give the same answer so either can be used.
Option 1
loadings_relative_A <- t(t(abs(loadings))/rowSums(t(abs(loadings))))*100
loadings_relative_A## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## bio_3 14.850656 6.414517 10.356458 33.544007 9.0085898 0.9942151 13.840312
## bio_7 17.109180 3.729291 12.053070 14.159247 19.1765473 1.8019146 24.811665
## bio_8 7.802604 20.544845 6.247980 4.496230 6.1800138 40.2763959 2.836495
## bio_9 11.887131 4.349147 37.772179 13.078961 6.9101503 8.0137969 8.857181
## bio_14 11.101071 18.191180 3.083048 10.036777 19.9384510 22.2996049 3.323073
## bio_15 9.369054 20.880380 6.091511 3.365956 0.9115813 18.7166455 10.846831
## bio_18 16.267425 9.200637 15.196677 6.096127 15.7167679 2.0413594 15.056066
## elev 11.612878 16.690003 9.199078 15.222695 22.1578987 5.8560679 20.428376
## PC8
## bio_3 0.3835837
## bio_7 3.3281622
## bio_8 4.4111546
## bio_9 3.6289524
## bio_14 22.7072114
## bio_15 32.7980990
## bio_18 28.6727578
## elev 4.0700790Option 2
loadings_relative_B <- sweep(x = abs(loadings),
MARGIN = 2,
STATS = colSums(abs(loadings)), FUN = "/")*100
loadings_relative_B## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## bio_3 14.850656 6.414517 10.356458 33.544007 9.0085898 0.9942151 13.840312
## bio_7 17.109180 3.729291 12.053070 14.159247 19.1765473 1.8019146 24.811665
## bio_8 7.802604 20.544845 6.247980 4.496230 6.1800138 40.2763959 2.836495
## bio_9 11.887131 4.349147 37.772179 13.078961 6.9101503 8.0137969 8.857181
## bio_14 11.101071 18.191180 3.083048 10.036777 19.9384510 22.2996049 3.323073
## bio_15 9.369054 20.880380 6.091511 3.365956 0.9115813 18.7166455 10.846831
## bio_18 16.267425 9.200637 15.196677 6.096127 15.7167679 2.0413594 15.056066
## elev 11.612878 16.690003 9.199078 15.222695 22.1578987 5.8560679 20.428376
## PC8
## bio_3 0.3835837
## bio_7 3.3281622
## bio_8 4.4111546
## bio_9 3.6289524
## bio_14 22.7072114
## bio_15 32.7980990
## bio_18 28.6727578
## elev 4.0700790Plotting the PCA
Set theme
First, I made a theme to change the background of the plot. Next, I changed the plot margins and the text size.
theme <- theme(panel.background = element_blank(),
panel.border=element_rect(fill=NA),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
strip.background=element_blank(),
axis.ticks=element_line(colour="black"),
plot.margin=unit(c(1,1,1,1),"line"),
axis.text = element_text(size = 12),
legend.text = element_text(size = 12),
legend.title = element_text(size = 12),
text = element_text(size = 12))Set color palette
pal <- pal_locuszoom()(4)ggbiplot
Next, use ggbiplot where obs.scale indicates the scale factor to apply to observation, var.scale indicates the scale factor to apply to variables, ellipse as TRUE draws a normal data ellipse for each group, and circle as TRUE draws a correlation circle.
g <- ggbiplot(data.pca, obs.scale = 1, var.scale = 1,
groups = data.species, ellipse = TRUE, circle = TRUE) +
scale_color_manual(name = '', values = pal) +
theme(legend.direction = 'vertical', legend.position = 'right',
legend.text = element_text(size = 12, face = "italic")) +
theme
g
ANOVA
Simple function to run an ANOVA and a post-hoc Tukey-HSD test
stat.test <- function(data = ptExtracteddf, x = "name", y){
bioaov <- aov(as.formula(paste0(y,"~",x)), data = data)
TH <- TukeyHSD(bioaov, "name")
m <- multcompLetters(TH$name[,4])
groups <- data.frame(groups = m$Letters, name = names(m$Letters))
return(groups)
}Run for bio_3 only
bio3aovplot <- ggplot(ptExtracteddf, aes(x = name, y = bio_3)) +
geom_boxplot(aes(fill = name)) +
scale_color_manual(name = '', values = pal) +
geom_text(data = stat.test(y = "bio_3"),
mapping = aes(x = name,
y = max(ptExtracteddf["bio_3"]+1),
label = groups),
size = 5, inherit.aes = FALSE) +
theme(axis.text.x = element_text(angle = 90, size = 8, face = 'italic'))
bio3aovplot
Loop through all variables
variablelist <- colnames(ptExtracteddf)[1:8]
plotlist <- c()
for(i in 1:8){
bio <- variablelist[i]
tempdf <- ptExtracteddf %>%
dplyr::select(name, variablelist[i])
plotlist[[i]] <- ggplot(tempdf, aes(x = name, y = tempdf[,2])) +
geom_boxplot(aes(fill = name)) +
scale_colour_manual(name = 'Species', values = pal) +
geom_text(data = stat.test(y = variablelist[i]),
mapping = aes(x = name,
y = max(tempdf[,2]+1),
label = groups),
size = 5, inherit.aes = FALSE) +
scale_x_discrete(labels = c('G', 'Pba','Pbr', 'S')) +
ggtitle(label = paste0(variablelist[i])) +
ylab(paste0(variablelist[i])) +
theme(legend.position = "none")
}
gridExtra::grid.arrange(grobs = plotlist)
Ecospat Niche Overlap and Niche Equivalency
Set up background points
bg1 <- randomPoints(mask = envtStack, n = 1000, p = alldf[,3:2])
bg1.env <- raster::extract(envtStack, bg1)
bg1.env <- data.frame(bg1.env)
allpt.bioclim <- rbind(bg1.env, data.bioclim) dudi.PCA to reduce variables
pca.env <- dudi.pca(allpt.bioclim,
center = TRUE, # Center by the mean
scannf = FALSE, # Don't plot
nf = 2) # Number of axis to keep Pull out scores for each species
p1.score <- suprow(pca.env, dplyr::filter(ptExtracteddf, name == "Galax urceolata")[, 1:8])$li
p2.score <- suprow(pca.env, dplyr::filter(ptExtracteddf, name == "Pyxidanthera barbulata")[, 1:8])$li
p3.score <- suprow(pca.env, dplyr::filter(ptExtracteddf, name == "Pyxidanthera brevifolia")[, 1:8])$li
p4.score <- suprow(pca.env, dplyr::filter(ptExtracteddf, name == "Shortia galacifolia")[, 1:8])$li
scores.clim <- pca.env$liVisualize
plot(scores.clim, pch = 16, asp = 1,
col = adjustcolor(1, alpha.f = 0.2), cex = 2,
xlab = "PC1", ylab = "PC2")
points(p1.score, pch = 18, col = pal[1], cex = 2)
points(p2.score, pch = 18, col = pal[2], cex = 2)
points(p3.score, pch = 18, col = pal[3], cex = 2)
points(p4.score, pch = 18, col = pal[4], cex = 2)
Kernel density estimates
Create occurrence density grids based on the ordination data.
z1 <- ecospat.grid.clim.dyn(scores.clim, scores.clim, p1.score, R = 100)
z2 <- ecospat.grid.clim.dyn(scores.clim, scores.clim, p2.score, R = 100)
z3 <- ecospat.grid.clim.dyn(scores.clim, scores.clim, p3.score, R = 100)
z4 <- ecospat.grid.clim.dyn(scores.clim, scores.clim, p4.score, R = 100)
zlist <- list(z1, z2, z3, z4)Niche Overlap
Schoener’s D ranges from 0 to 1. 0 represents no similarity between niche space. 1 represents completely identical niche space.
overlapD <- matrix(ncol = 2, nrow = 7)
n <- 1
for(i in 1:3){
for(j in 2:4){
if(i != j){
overlapD[n, 1]<- paste0("z", i, "-", "z", j)
overlapD[n, 2]<- ecospat.niche.overlap(zlist[[i]], zlist[[j]], cor = TRUE)$D
n <- n + 1
}
}
}
overlapDdf <- data.frame(overlapD)
overlapDdf## X1 X2
## 1 z1-z2 0.145352580822439
## 2 z1-z3 0.00131113812012462
## 3 z1-z4 0.518715321036678
## 4 z2-z3 0.00337172752511095
## 5 z2-z4 0.241888138587862
## 6 z3-z2 0.00337172752511095
## 7 z3-z4 0.0022848212591513Niche Overlap Visualization
par(mfrow=c(1,2))
ecospat.plot.niche.dyn(z1, z4, quant=0.25, interest = 1
, title= "Niche Overlap - Z1 top", name.axis1="PC1", name.axis2="PC2")
ecospat.plot.niche.dyn(z1, z4, quant=0.25, interest = 2
, title= "Niche Overlap - Z4 top", name.axis1="PC1", name.axis2="PC2")
Niche Equivalency Test
Based on Warren et al. 2008 - Are the two niche identical?
Hypothesis test for D, null based on randomization. H1: the niche overlap is higher than expected by chance (or when randomized).
eq.test <- ecospat.niche.equivalency.test(z1, z4, rep = 10, alternative = "greater")
ecospat.plot.overlap.test(eq.test, "D", "Equivalency")
Niche Similarity Test
Based on Warren et al. 2008 - Are the two niche similar?
Can one species’ niche predict the occurrences of a second species better than expected by chance?
sim.test <- ecospat.niche.similarity.test(z1, z4, rep = 10, alternative = "greater", rand.type=2)
ecospat.plot.overlap.test(sim.test, "D", "Similarity")
Ecological Niche Modeling
Ecological Niche Modeling in R.
Modified and created by Anthony Melton and ML Gaynor.
This script is for generating and testing ENMs using ENMEval. Please see the paper describing ENMEval and their vignette.
Set up java memory
options(java.parameters = "- Xmx16g") # increase memory that can be usedLoad Packages
library(raster)
library(gtools)
library(dplyr)
library(dismo)
library(ENMeval)
library(ggplot2)
library(viridis)Load Function
source("functions/ENMevaluation.R")Load data file
alldf <- read.csv("data/cleaning_demo/maxent_ready/diapensiaceae_maxentready_20210625.csv")Subset for each species
Galax_urceolata <- dplyr::filter(alldf, name == "Galax urceolata")
Pyxidanthera_barbulata <- dplyr::filter(alldf, name == "Pyxidanthera barbulata")
Pyxidanthera_brevifolia <- dplyr::filter(alldf, name == "Pyxidanthera brevifolia")
Shortia_galacifolia <- dplyr::filter(alldf, name == "Shortia galacifolia")Raster layers
list <- list.files("data/climate_processing/PresentLayers/all", full.names = TRUE, recursive = FALSE)
list <- mixedsort(sort(list))
allstack <- stack(list)Read in species training layers
gstack <- stack(mixedsort(sort(list.files("data/climate_processing/PresentLayers/Galax_urceolata/", full.names = TRUE))))
pbastack <- stack(mixedsort(sort(list.files("data/climate_processing/PresentLayers/Pyxidanthera_barbulata/", full.names = TRUE))))
pbrstack <- stack(mixedsort(sort(list.files("data/climate_processing/PresentLayers/Pyxidanthera_brevifolia/", full.names = TRUE))))
sstack <- stack(mixedsort(sort(list.files("data/climate_processing/PresentLayers/Shortia_galacifolia/", full.names = TRUE))))Fix projection
projection(allstack) <- "+proj=longlat +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +no_defs"
projection(gstack) <- "+proj=longlat +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +no_defs"
projection(pbastack) <- "+proj=longlat +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +no_defs"
projection(pbrstack) <- "+proj=longlat +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +no_defs"
projection(sstack) <- "+proj=longlat +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +no_defs"Model Generation
dismo Model Generations
Match MaxEnt GUI settings (see word document and powerpoint)
evaldis <- dismo::maxent(x = gstack, p = Galax_urceolata[, c("long", "lat")], nbg = 10000,
args = c("projectionlayers=data/climate_processing/PresentLayers/all",
"responsecurves", "jackknife",
"outputformat=logistic","randomseed",
"randomtestpoints=25", "replicates=5",
"replicatetype=subsample", "maximumiterations=5000",
"writebackgroundpredictions","responsecurvesexponent",
"writeplotdata"),
removeDuplicates = TRUE
#,path = "data/Ecological_Niche_Modeling/enm_output/Galax_urceolata/"
)## This is MaxEnt version 3.4.1ENMeval Model generation
ENMeval will generate multiple models and test them per the specified test method.Two important variables here are the regularization multiplier (RM) value and the feature class (FC). FC will allow for different shapes in response curves (linear, hinge, quadratic, product, and threshold) can be used in the model and RM will influence how many parameters are included in the model.
eval1 <- ENMeval::ENMevaluate(occ = Galax_urceolata[, c("long", "lat")],
env = gstack,
tune.args = list(fc = c("L","Q"), rm = 1:2),
partitions = "block",
n.bg = 10000,
parallel = FALSE,
algorithm = 'maxent.jar',
user.eval = proc)
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dismo
Inspect the dismo model based on the html
browseURL(evaldis@html)ENMeval
Inspect the many models
Visualize
maps <- eval1@predictions
plot(maps)
Look at model overlap
mod_overlap <- calc.niche.overlap(eval1@predictions, overlapStat = "D")##
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kable_styling(bootstrap_options = c("striped", "hover", "condensed", font_size = 10)) %>%
scroll_box(width = "100%", height = "200px")| fc.L_rm.1 | fc.Q_rm.1 | fc.L_rm.2 | fc.Q_rm.2 | |
|---|---|---|---|---|
| fc.L_rm.1 | NA | NA | NA | NA |
| fc.Q_rm.1 | 0.9536825 | NA | NA | NA |
| fc.L_rm.2 | 0.9850919 | 0.9522876 | NA | NA |
| fc.Q_rm.2 | 0.9515567 | 0.9863397 | 0.95379 | NA |
Inspect the results
Identify the best model selecting models with the lowest average test omission rate and the highest average validation AUC
results <- eval.results(eval1)
opt.seq <- results %>%
dplyr::filter(or.10p.avg == min(or.10p.avg)) %>%
dplyr::filter(auc.val.avg == max(auc.val.avg))
kable(opt.seq) %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", font_size = 10)) %>%
scroll_box(width = "100%", height = "200px")| fc | rm | tune.args | auc.train | cbi.train | auc.diff.avg | auc.diff.sd | auc.val.avg | auc.val.sd | cbi.val.avg | cbi.val.sd | or.10p.avg | or.10p.sd | or.mtp.avg | or.mtp.sd | proc_auc_ratio.avg | proc_auc_ratio.sd | proc_pval.avg | proc_pval.sd | AICc | delta.AICc | w.AIC | ncoef |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Q | 2 | fc.Q_rm.2 | 0.7779521 | 0.844 | 0.145921 | 0.0987648 | 0.7840933 | 0.1477396 | 0.77225 | 0.1242293 | 0.1527778 | 0.2373334 | 0.0138889 | 0.0277778 | 1.961035 | 0.0121882 | 0 | 0 | 1858.097 | 5.63308 | 0.0391711 | 7 |
Subset model
mod.seq <- eval.models(eval1)[[opt.seq$tune.args]]Inspect
kable(mod.seq@results) %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", font_size = 10)) %>%
scroll_box(width = "100%", height = "200px")| X.Training.samples | 72.0000 |
| Regularized.training.gain | 0.6478 |
| Unregularized.training.gain | 0.7199 |
| Iterations | 200.0000 |
| Training.AUC | 0.7760 |
| X.Background.points | 10070.0000 |
| bio_14.contribution | 21.1306 |
| bio_15.contribution | 1.9885 |
| bio_18.contribution | 2.6430 |
| bio_3.contribution | 15.7797 |
| bio_7.contribution | 4.4342 |
| bio_8.contribution | 1.7713 |
| bio_9.contribution | 1.0230 |
| elev.contribution | 51.2297 |
| bio_14.permutation.importance | 0.0000 |
| bio_15.permutation.importance | 33.4249 |
| bio_18.permutation.importance | 4.7675 |
| bio_3.permutation.importance | 28.0884 |
| bio_7.permutation.importance | 33.6544 |
| bio_8.permutation.importance | 0.0647 |
| bio_9.permutation.importance | 0.0000 |
| elev.permutation.importance | 0.0000 |
| Entropy | 8.5695 |
| Prevalence..average.probability.of.presence.over.background.sites. | 0.3160 |
| Fixed.cumulative.value.1.cumulative.threshold | 1.0000 |
| Fixed.cumulative.value.1.Cloglog.threshold | 0.0779 |
| Fixed.cumulative.value.1.area | 0.9181 |
| Fixed.cumulative.value.1.training.omission | 0.0139 |
| Fixed.cumulative.value.5.cumulative.threshold | 5.0000 |
| Fixed.cumulative.value.5.Cloglog.threshold | 0.1730 |
| Fixed.cumulative.value.5.area | 0.7646 |
| Fixed.cumulative.value.5.training.omission | 0.0556 |
| Fixed.cumulative.value.10.cumulative.threshold | 10.0000 |
| Fixed.cumulative.value.10.Cloglog.threshold | 0.2321 |
| Fixed.cumulative.value.10.area | 0.6498 |
| Fixed.cumulative.value.10.training.omission | 0.1111 |
| Minimum.training.presence.cumulative.threshold | 0.9924 |
| Minimum.training.presence.Cloglog.threshold | 0.0778 |
| Minimum.training.presence.area | 0.9185 |
| Minimum.training.presence.training.omission | 0.0000 |
| X10.percentile.training.presence.cumulative.threshold | 7.5963 |
| X10.percentile.training.presence.Cloglog.threshold | 0.2064 |
| X10.percentile.training.presence.area | 0.7004 |
| X10.percentile.training.presence.training.omission | 0.0972 |
| Equal.training.sensitivity.and.specificity.cumulative.threshold | 34.3959 |
| Equal.training.sensitivity.and.specificity.Cloglog.threshold | 0.3760 |
| Equal.training.sensitivity.and.specificity.area | 0.2974 |
| Equal.training.sensitivity.and.specificity.training.omission | 0.2917 |
| Maximum.training.sensitivity.plus.specificity.cumulative.threshold | 56.1734 |
| Maximum.training.sensitivity.plus.specificity.Cloglog.threshold | 0.5194 |
| Maximum.training.sensitivity.plus.specificity.area | 0.0998 |
| Maximum.training.sensitivity.plus.specificity.training.omission | 0.4028 |
| Balance.training.omission..predicted.area.and.threshold.value.cumulative.threshold | 0.9924 |
| Balance.training.omission..predicted.area.and.threshold.value.Cloglog.threshold | 0.0778 |
| Balance.training.omission..predicted.area.and.threshold.value.area | 0.9185 |
| Balance.training.omission..predicted.area.and.threshold.value.training.omission | 0.0000 |
| Equate.entropy.of.thresholded.and.original.distributions.cumulative.threshold | 17.3506 |
| Equate.entropy.of.thresholded.and.original.distributions.Cloglog.threshold | 0.2860 |
| Equate.entropy.of.thresholded.and.original.distributions.area | 0.5231 |
| Equate.entropy.of.thresholded.and.original.distributions.training.omission | 0.1944 |
Look at variable contribution
plot(mod.seq)
Look at the response curves
dismo::response(mod.seq)## This is MaxEnt version 3.4.1## This is MaxEnt version 3.4.1## This is MaxEnt version 3.4.1## This is MaxEnt version 3.4.1## This is MaxEnt version 3.4.1## This is MaxEnt version 3.4.1## This is MaxEnt version 3.4.1## This is MaxEnt version 3.4.1
Project model to allstack
p <- predict(mod.seq, allstack) ## This is MaxEnt version 3.4.1Visualize
# Make into a data frame
p_df <- as.data.frame(p, xy = TRUE)
# Plot
ggplot() +
geom_raster(data = p_df, aes(x = x, y = y, fill = layer)) +
geom_point(data= Galax_urceolata,
mapping = aes(x = long, y = lat),
col='red', cex=0.05) +
coord_quickmap() +
theme_bw() +
scale_fill_gradientn(colours = viridis::viridis(99),
na.value = "black")
Save outputs
R saved dataset
save(mod.seq, file = "data/Ecological_Niche_Modeling/enm_output/ENMeval/GalaxENM.rda")Save Raster
writeRaster(x = p, filename = "data/Ecological_Niche_Modeling/enm_output/ENMeval/GalaxENM.asc",
format = "ascii", NAFlag = "-9999", overwrite = T)ENM Processing
Processing Ecological Niche Models in R.
Script by Anthony Melton and ML Gaynor.
Load Packages
library(raster)
library(gtools)
library(dplyr)
library(ENMTools)
library(ENMeval)
library(ape)
library(RStoolbox)
library(hypervolume)
library(phytools)Load functions
The following command generates a binary predicted occurrence map. This was written by Anthony Melton.
source("functions/Functions_AEM.R")Read in models you generated with the MaxEnt GUI into R.
sp1_enm.mx.b <- raster("data/Ecological_Niche_Modeling/enm_output/Galax_urceolata_avg.asc")
sp2_enm.mx.b <- raster("data/Ecological_Niche_Modeling/enm_output/Pyxidanthera_barbulata_avg.asc")
sp3_enm.mx.b <- raster("data/Ecological_Niche_Modeling/enm_output/Pyxidanthera_brevifolia_avg.asc")
sp4_enm.mx.b <- raster("data/Ecological_Niche_Modeling/enm_output/Shortia_galacifolia_avg.asc")Read in Bioclim layers
These steps are described in the previous script
system("rm -rf data/climate_processing/PresentLayers/all/maxent.cache/")
list <- list.files("data/climate_processing/PresentLayers/all/",
full.names = TRUE, recursive = FALSE)
list <- mixedsort(sort(list))
allstack <- stack(list)
projection(allstack) <- "+proj=longlat +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +no_defs"ENM Breadth
Niche breadth is the breadth of environmental factors for a species’ niche, ranging from 0 to 1. When breadth is closer to 1 the more generalist species with wider tolerances. Values closer to 0 indicate a more specialized species. The raster.breadth command in ENMTools measures the smoothness of suitability scores across a projected landscape. The higher the score, the more of the available niche space a species occupies.
sp1_breadth <- ENMTools::raster.breadth(x = sp1_enm.mx.b)
sp1_breadth$B2## [1] 0.6369572sp2_breadth <- ENMTools::raster.breadth(x = sp2_enm.mx.b)
sp2_breadth$B2## [1] 0.1665568sp3_breadth <- ENMTools::raster.breadth(x = sp3_enm.mx.b)
sp3_breadth$B2## [1] 0.04380163sp4_breadth <- ENMTools::raster.breadth(x = sp4_enm.mx.b)
sp4_breadth$B2## [1] 0.4567ENM Overlap
Calculating niche overlap, Schoener’s D, with ENMEval - Schoener’s D ranges from 0 to 1, where 0 represents no similarity between the projections and 1 represents completely identical projections.
Stack the projections and make sure the stack is named.
enm_stack.b <- stack(sp1_enm.mx.b, sp2_enm.mx.b, sp3_enm.mx.b, sp4_enm.mx.b)
names(enm_stack.b) <- c("Galax urceolata", "Pyxidanthera barbulata", "Pyxidanthera brevifolia","Shortia galacifolia" )Calculate
calc.niche.overlap(enm_stack.b, overlapStat = "D")##
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|======================================================================| 100%## Galax.urceolata Pyxidanthera.barbulata
## Galax.urceolata NA NA
## Pyxidanthera.barbulata 0.2108513 NA
## Pyxidanthera.brevifolia 0.1287981 0.1614858
## Shortia.galacifolia 0.6447093 0.1228339
## Pyxidanthera.brevifolia Shortia.galacifolia
## Galax.urceolata NA NA
## Pyxidanthera.barbulata NA NA
## Pyxidanthera.brevifolia NA NA
## Shortia.galacifolia 0.03923816 NAPhylogenetic correlations
Let’s look at niche overlap over a tree!
Load the tree file
tree <- ape::read.tree(file =
"data/Ecological_Niche_Modeling/AOC_Test_Demo/diapensiaceae_subset.tre")
tree <- ape::compute.brlen(phy = tree, method = "grafen")
plot(tree)
Drop the outgroup
tree <- drop.tip(tree, "Cyrilla_racemiflora")
plot(tree)
Generate species objects for each tree member!
sp1 <- enmtools.species(species.name = "Galax_urceolata",
presence.points = Galax_urceolata[,3:2])
sp1$range <- background.raster.buffer(sp1$presence.points, 25000, mask = allstack)
sp1$background.points = background.points.buffer(points = sp1$presence.points, radius = 5000, n = 10000, mask = allstack[[1]])
##############################
sp2 <- enmtools.species(species.name = "Pyxidanthera_barbulata",
presence.points = Pyxidanthera_barbulata[,3:2])
sp2$range <- background.raster.buffer(sp2$presence.points, 25000, mask = allstack)
sp2$background.points = background.points.buffer(points = sp2$presence.points, radius = 5000, n = 10000, mask = allstack[[1]])
#############################
sp3 <- enmtools.species(species.name = "Pyxidanthera_brevifolia",
presence.points = Pyxidanthera_brevifolia[,3:2])
sp3$range <- background.raster.buffer(sp3$presence.points, 25000, mask = allstack)
sp3$background.points = background.points.buffer(points = sp3$presence.points, radius = 5000, n = 10000, mask = allstack[[1]])
#############################
sp4 <- enmtools.species(species.name = "Shortia_galacifolia",
presence.points = Shortia_galacifolia[,3:2])
sp4$range <- background.raster.buffer(sp4$presence.points, 25000, mask = allstack)
sp4$background.points = background.points.buffer(points = sp4$presence.points, radius = 5000, n = 10000, mask = allstack[[1]])Create “clade” object with all the species in the tree
clade=enmtools.clade(species = list(sp1, sp2, sp3, sp4), tree = tree)
check.clade(clade)
##
##
## An enmtools.clade object with 4 species
##
## Species names:
## Shortia_galacifolia Pyxidanthera_brevifolia Pyxidanthera_barbulata Galax_urceolata
##
## Tree:
##
## Phylogenetic tree with 4 tips and 3 internal nodes.
##
## Tip labels:
## Shortia_galacifolia, Pyxidanthera_brevifolia, Pyxidanthera_barbulata, Galax_urceolata
##
## Rooted; includes branch lengths.
##
##
## Data Summary:
## <table>
## <thead>
## <tr>
## <th style="text-align:left;"> </th>
## <th style="text-align:left;"> species.names </th>
## <th style="text-align:left;"> in.tree </th>
## <th style="text-align:left;"> presence </th>
## <th style="text-align:left;"> background </th>
## <th style="text-align:left;"> range </th>
## </tr>
## </thead>
## <tbody>
## <tr>
## <td style="text-align:left;"> Shortia_galacifolia </td>
## <td style="text-align:left;"> Shortia_galacifolia </td>
## <td style="text-align:left;"> TRUE </td>
## <td style="text-align:left;"> 12 </td>
## <td style="text-align:left;"> 10000 </td>
## <td style="text-align:left;"> present </td>
## </tr>
## <tr>
## <td style="text-align:left;"> Pyxidanthera_brevifolia </td>
## <td style="text-align:left;"> Pyxidanthera_brevifolia </td>
## <td style="text-align:left;"> TRUE </td>
## <td style="text-align:left;"> 33 </td>
## <td style="text-align:left;"> 10000 </td>
## <td style="text-align:left;"> present </td>
## </tr>
## <tr>
## <td style="text-align:left;"> Pyxidanthera_barbulata </td>
## <td style="text-align:left;"> Pyxidanthera_barbulata </td>
## <td style="text-align:left;"> TRUE </td>
## <td style="text-align:left;"> 234 </td>
## <td style="text-align:left;"> 10000 </td>
## <td style="text-align:left;"> present </td>
## </tr>
## <tr>
## <td style="text-align:left;"> Galax_urceolata </td>
## <td style="text-align:left;"> Galax_urceolata </td>
## <td style="text-align:left;"> TRUE </td>
## <td style="text-align:left;"> 72 </td>
## <td style="text-align:left;"> 10000 </td>
## <td style="text-align:left;"> present </td>
## </tr>
## </tbody>
## </table>Age-Range Correlation Test
range.aoc <- enmtools.aoc(clade = clade, nreps = 10, overlap.source = "range")## Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> =
## "none")` instead.
## Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> =
## "none")` instead.summary(range.aoc)##
##
## Age-Overlap Correlation test
##
## 10 replicates
##
## p values:
## (Intercept) empirical.df$age
## 0.1363636 0.1363636
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## NULLAge-Overlap Correlation Test
glm.aoc <- enmtools.aoc(clade = clade, env = allstack, nreps = 10, overlap.source = "glm")## Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> =
## "none")` instead.
## Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> =
## "none")` instead.summary(glm.aoc)##
##
## Age-Overlap Correlation test
##
## 10 replicates
##
## p values:
## (Intercept) empirical.df$age
## 0.5 0.5
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## NULLThe results here are pretty meaningless since we’re looking at very few species, but it serves the purpose of the demo. For range AOCs, an intercept >0.5 and negative slope are indicative of sympatric species, while an intercept of <0.5 and a positive slope are indicative on non-sympatric speciation. A low intercept and positive slope for niche overlap would indicate niche divergence. ***
Hypervolume
This will generate a binary map with any cell that contains a suitability score greater than or equal to the lowest score with an occurrence point as a presence. There are other ways to set the threshold, including percentiles or training statistics.
Load occurrence data
alldf <- read.csv("data/cleaning_demo/maxent_ready/diapensiaceae_maxentready_20210625.csv")Subset for each species
Galax_urceolata <- dplyr::filter(alldf, name == "Galax urceolata")
Pyxidanthera_barbulata <- dplyr::filter(alldf, name == "Pyxidanthera barbulata")
Pyxidanthera_brevifolia <- dplyr::filter(alldf, name == "Pyxidanthera brevifolia")
Shortia_galacifolia <- dplyr::filter(alldf, name == "Shortia galacifolia")Create the binary maps
sp1.dist <- make.binary.map(model = sp1_enm.mx.b, occ.dat = Galax_urceolata)
sp2.dist <- make.binary.map(model = sp2_enm.mx.b, occ.dat = Pyxidanthera_barbulata)
sp3.dist <- make.binary.map(model = sp3_enm.mx.b, occ.dat = Pyxidanthera_brevifolia)
sp4.dist <- make.binary.map(model = sp4_enm.mx.b, occ.dat = Shortia_galacifolia)Plot
par(mfrow = c(2,2), mar = c(1,1,1,1))
plot(sp1.dist)
plot(sp2.dist)
plot(sp3.dist)
plot(sp4.dist)
Hypervolume
Next, let’s work on getting some data from the predicted distributions! Niche space can be thought of as a multi-dimensional hypervolume. We’re using climatic data in this case, so we’re measuring the hypervolume of climatic niche space occupied by these species.
Warning: This takes forever!
sp1.hv <- get_hypervolume(binary_projection = sp1.dist, envt = allstack)
sp2.hv <- get_hypervolume(binary_projection = sp2.dist, envt = allstack)
sp3.hv <- get_hypervolume(binary_projection = sp3.dist, envt = allstack)
sp4.hv <- get_hypervolume(binary_projection = sp4.dist, envt = allstack)Compare the hypervolumes
hv_set <- hypervolume_set(hv1 = sp1.hv, hv2 = sp2.hv, check.memory = F)
hypervolume_overlap_statistics(hv_set)
plot(hv_set)
get_volume(hv_set)Phylogenetic Diversity
Original script by J. Janzten.
Modified by ML Gaynor.
Load Packages
library(raster)
library(ape)
library(phytools)
library(picante)
library(ggplot2)
library(dplyr)
library(tidyverse)
library(rgdal)
library(rgeos)
library(sp)Load the tree file
tree <- ape::read.tree(file = "data/Ecological_Niche_Modeling/AOC_Test_Demo/diapensiaceae_subset.tre")
tree <- ape::compute.brlen(phy = tree, method = "grafen")
plot(tree)
Drop the outgroup
tree <- drop.tip(tree, "Cyrilla_racemiflora")Read ENM models
sp1_enm.mx.b <- raster("data/Ecological_Niche_Modeling/enm_output/Galax_urceolata_avg.asc")
sp2_enm.mx.b <- raster("data/Ecological_Niche_Modeling/enm_output/Pyxidanthera_barbulata_avg.asc")
sp3_enm.mx.b <- raster("data/Ecological_Niche_Modeling/enm_output/Pyxidanthera_brevifolia_avg.asc")
sp4_enm.mx.b <- raster("data/Ecological_Niche_Modeling/enm_output/Shortia_galacifolia_avg.asc")Reclassify rasters
reclassify_raster <- function(OGraster){
## Reclassify the raster by the suitability score
OGraster[OGraster >= 0.25] <- 1
OGraster[OGraster < 0.25] <- 0
OGraster[is.na(OGraster)] <- 0
crs(OGraster) <- "+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0"
return(OGraster)
}sp1_enm.mx.b <- reclassify_raster(sp1_enm.mx.b)
sp2_enm.mx.b <- reclassify_raster(sp2_enm.mx.b)
sp3_enm.mx.b <- reclassify_raster(sp3_enm.mx.b)
sp4_enm.mx.b <- reclassify_raster(sp4_enm.mx.b)Inspect the reclassified layers
par(mfrow=c(2,2))
plot(sp1_enm.mx.b)
plot(sp2_enm.mx.b)
plot(sp3_enm.mx.b)
plot(sp4_enm.mx.b)
Stack rasters and create dataframe
enm_stack <- stack(sp1_enm.mx.b, sp2_enm.mx.b, sp3_enm.mx.b, sp4_enm.mx.b)
names(enm_stack) <- c("Galax_urceolata", "Pyxidanthera_barbulata", "Pyxidanthera_brevifolia","Shortia_galacifolia" )Convert to dataframe
rasterstack_df <- as.data.frame(enm_stack, xy = TRUE)Extract Ecoregions for points
Load USA shapefile
Obtained at https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states
shp <- ("data/phylogenetic_diversity/USraster/us_eco_l4/us_eco_l4_no_st.shp")
USAshape <- readOGR(shp, layer="us_eco_l4_no_st")## OGR data source with driver: ESRI Shapefile
## Source: "/Users/shellygaynor/Dropbox/Workshops/BotanyENMWorkshop2021/Demos/Rbased/CrashCourse/data/phylogenetic_diversity/USraster/us_eco_l4/us_eco_l4_no_st.shp", layer: "us_eco_l4_no_st"
## with 5896 features
## It has 16 fieldsCorrect CRS
newcrs <- "+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0"
USAshape2 <- spTransform(USAshape, newcrs)Convert dataframe to points
df.sp <- rasterstack_df[,1:2]
coordinates(df.sp)<-~x+y
crs(df.sp) <- "+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0"Extract points from shapefile
Warning: this will take a while!!
df.sp.info <- sp::over(df.sp, USAshape2)Bind dataframe with points and with ecosystems
rasterstack_df.info <- cbind(df.sp.info, rasterstack_df)
rasterstack_df.info2 <- rasterstack_df.info %>%
dplyr::group_by(L4_KEY) %>%
dplyr::summarize(Galax_urceolata = max(Galax_urceolata),
Pyxidanthera_barbulata = max(Pyxidanthera_barbulata),
Pyxidanthera_brevifolia = max(Pyxidanthera_brevifolia),
Shortia_galacifolia = max(Shortia_galacifolia))
rasterstack_df.info2df <- as.data.frame(rasterstack_df.info2, row.names = FALSE)Removing NA
rasterstack_df.info4 <- rasterstack_df.info2df[1:137,]Removing the first column
rasterstack_df.info3 <- rasterstack_df.info4[,-1]Renaming the rows
row.names(rasterstack_df.info3) <- NULL
row.names(rasterstack_df.info3) <- rasterstack_df.info4[,1]
kable(rasterstack_df.info3)%>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", font_size = 10)) %>%
scroll_box(width = "100%", height = "200px")| Galax_urceolata | Pyxidanthera_barbulata | Pyxidanthera_brevifolia | Shortia_galacifolia | |
|---|---|---|---|---|
| 45a Southern Inner Piedmont | 1 | 0 | 0 | 1 |
| 45b Southern Outer Piedmont | 1 | 0 | 1 | 1 |
| 45c Carolina Slate Belt | 1 | 0 | 1 | 1 |
| 45d Talladega Upland | 1 | 0 | 0 | 0 |
| 45e Northern Inner Piedmont | 1 | 0 | 0 | 1 |
| 45f Northern Outer Piedmont | 0 | 0 | 1 | 1 |
| 45g Triassic Basins | 1 | 0 | 1 | 1 |
| 45i Kings Mountain | 1 | 0 | 0 | 1 |
| 54a Illinois/Indiana Prairies | 0 | 0 | 0 | 0 |
| 54b Chicago Lake Plain | 0 | 0 | 0 | 0 |
| 54c Kankakee Marsh | 0 | 0 | 0 | 0 |
| 54d Sand Area | 0 | 0 | 0 | 0 |
| 54f Valparaiso-Wheaton Morainal Complex | 0 | 0 | 0 | 0 |
| 55a Clayey High Lime Till Plains | 0 | 0 | 0 | 0 |
| 55b Loamy High Lime Till Plains | 0 | 0 | 0 | 0 |
| 55c Mad River Interlobate Area | 0 | 0 | 0 | 0 |
| 55d Pre-Wisconsinan Drift Plains | 0 | 0 | 0 | 0 |
| 55e Darby Plains | 0 | 0 | 0 | 0 |
| 55f Whitewater Interlobate Area | 0 | 0 | 0 | 0 |
| 56a Northern Indiana Lake Country | 0 | 0 | 0 | 0 |
| 56b Battle Creek/Elkhart Outwash Plain | 0 | 0 | 0 | 0 |
| 56c Middle Tippecanoe Plains | 0 | 0 | 0 | 0 |
| 56d Michigan Lake Plain | 0 | 0 | 0 | 0 |
| 57a Maumee Lake Plain | 0 | 0 | 0 | 0 |
| 57b Oak Openings | 0 | 0 | 0 | 0 |
| 57c Paulding Plains | 0 | 0 | 0 | 0 |
| 57d Marblehead Drift/Limestone Plain | 0 | 0 | 0 | 0 |
| 58b Western New England Marble Valleys | 0 | 0 | 0 | 0 |
| 58e Berkshire Transition | 0 | 0 | 0 | 0 |
| 58h Reading Prong | 0 | 0 | 0 | 0 |
| 58i Glaciated Reading Prong/Hudson Highlands | 0 | 0 | 0 | 0 |
| 58x Taconic Foothills | 0 | 0 | 0 | 0 |
| 59a Connecticut Valley | 0 | 0 | 0 | 1 |
| 59c Southern New England Coastal Plains and Hills | 0 | 1 | 0 | 1 |
| 59g Long Island Sound Coastal Lowland | 0 | 1 | 0 | 1 |
| 59i Hudson Valley | 0 | 0 | 0 | 0 |
| 60a Glaciated Low Allegheny Plateau | 0 | 0 | 0 | 0 |
| 60b Delaware-Neversink Highlands | 0 | 0 | 0 | 0 |
| 61b Mosquito Creek/Pymatuning Lowlands | 0 | 0 | 0 | 0 |
| 61c Low Lime Drift Plain | 0 | 0 | 0 | 0 |
| 61d Erie Gorges | 0 | 0 | 0 | 0 |
| 61e Summit Interlobate Area | 0 | 0 | 0 | 0 |
| 62a Pocono High Plateau | 0 | 0 | 0 | 0 |
| 62b Low Poconos | 0 | 0 | 0 | 0 |
| 62c Glaciated High Allegheny Plateau | 0 | 0 | 0 | 0 |
| 62d Unglaciated High Allegheny Plateau | 0 | 0 | 0 | 0 |
| 63a Delaware River Terraces and Uplands | 0 | 1 | 0 | 1 |
| 63b Chesapeake-Pamlico Lowlands and Tidal Marshes | 1 | 1 | 0 | 1 |
| 63c Swamps and Peatlands | 1 | 1 | 0 | 0 |
| 63d Virginian Barrier Islands and Coastal Marshes | 1 | 1 | 0 | 0 |
| 63e Mid-Atlantic Flatwoods | 1 | 1 | 0 | 0 |
| 63f Delmarva Uplands | 0 | 1 | 0 | 0 |
| 63g Carolinian Barrier Islands and Coastal Marshes | 1 | 1 | 1 | 0 |
| 63h Carolina Flatwoods | 1 | 1 | 1 | 0 |
| 63n Mid-Atlantic Floodplains and Low Terraces | 1 | 1 | 0 | 0 |
| 64a Triassic Lowlands | 0 | 1 | 0 | 1 |
| 64b Trap Rock and Conglomerate Uplands | 0 | 1 | 0 | 1 |
| 64c Piedmont Uplands | 0 | 1 | 0 | 0 |
| 64d Piedmont Limestone/Dolomite Lowlands | 0 | 1 | 0 | 0 |
| 64e Glaciated Triassic Lowlands | 0 | 1 | 0 | 0 |
| 64f Passaic Basin Freshwater Wetlands | 0 | 1 | 0 | 0 |
| 64g Hackensack Meadowlands | 0 | 1 | 0 | 0 |
| 65c Sand Hills | 1 | 1 | 1 | 1 |
| 65i Fall Line Hills | 0 | 0 | 0 | 0 |
| 65j Transition Hills | 0 | 0 | 0 | 0 |
| 65l Atlantic Southern Loam Plains | 1 | 1 | 1 | 0 |
| 65m Rolling Coastal Plain | 1 | 1 | 1 | 1 |
| 65n Chesapeake Rolling Coastal Plain | 0 | 1 | 0 | 1 |
| 65p Southeastern Floodplains and Low Terraces | 1 | 0 | 1 | 0 |
| 66a Northern Igneous Ridges | 1 | 0 | 0 | 1 |
| 66b Northern Sedimentary and Metasedimentary Ridges | 1 | 0 | 0 | 1 |
| 66c New River Plateau | 1 | 0 | 0 | 1 |
| 66d Southern Crystalline Ridges and Mountains | 1 | 0 | 0 | 1 |
| 66e Southern Sedimentary Ridges | 1 | 0 | 0 | 1 |
| 66f Limestone Valleys and Coves | 1 | 0 | 0 | 1 |
| 66g Southern Metasedimentary Mountains | 1 | 0 | 0 | 1 |
| 66i High Mountains | 1 | 0 | 0 | 1 |
| 66j Broad Basins | 1 | 0 | 0 | 1 |
| 66k Amphibolite Mountains | 1 | 0 | 0 | 1 |
| 66l Eastern Blue Ridge Foothills | 1 | 0 | 0 | 1 |
| 66m Sauratown Mountains | 1 | 0 | 0 | 1 |
| 67a Northern Limestone/Dolomite Valleys | 1 | 0 | 0 | 1 |
| 67b Northern Shale Valleys | 1 | 0 | 0 | 1 |
| 67c Northern Sandstone Ridges | 1 | 0 | 0 | 1 |
| 67d Northern Dissected Ridges and Knobs | 1 | 0 | 0 | 1 |
| 67e Anthracite Subregion | 0 | 0 | 0 | 0 |
| 67f Southern Limestone/Dolomite Valleys and Low Rolling Hills | 1 | 0 | 0 | 1 |
| 67g Southern Shale Valleys | 1 | 0 | 0 | 1 |
| 67h Southern Sandstone Ridges | 1 | 0 | 0 | 1 |
| 67i Southern Dissected Ridges and Knobs | 1 | 0 | 0 | 1 |
| 67j Northern Glaciated Limestone Valleys | 0 | 0 | 0 | 0 |
| 67k Northern Glaciated Shale and Slate Valleys | 0 | 0 | 0 | 0 |
| 67l Northern Glaciated Limestone Ridges, Valleys, and Terraces | 0 | 0 | 0 | 0 |
| 67m Northern Glaciated Ridges | 0 | 0 | 0 | 0 |
| 68a Cumberland Plateau | 1 | 0 | 0 | 1 |
| 68b Sequatchie Valley | 1 | 0 | 0 | 0 |
| 68c Plateau Escarpment | 1 | 0 | 0 | 1 |
| 68d Southern Table Plateaus | 1 | 0 | 0 | 0 |
| 68e Dissected Plateau | 0 | 0 | 0 | 0 |
| 68f Shale Hills | 0 | 0 | 0 | 0 |
| 69a Forested Hills and Mountains | 1 | 0 | 0 | 1 |
| 69b Uplands and Valleys of Mixed Land Use | 0 | 0 | 0 | 0 |
| 69c Greenbriar Karst | 1 | 0 | 0 | 1 |
| 69d Dissected Appalachian Plateau | 1 | 0 | 0 | 1 |
| 69e Cumberland Mountain Thrust Block | 1 | 0 | 0 | 1 |
| 70a Permian Hills | 0 | 0 | 0 | 0 |
| 70b Monongahela Transition Zone | 1 | 0 | 0 | 0 |
| 70c Pittsburgh Low Plateau | 0 | 0 | 0 | 0 |
| 70d Knobs-Lower Scioto Dissected Plateau | 0 | 0 | 0 | 0 |
| 70e Unglaciated Upper Muskingum Basin | 0 | 0 | 0 | 0 |
| 70f Ohio/Kentucky Carboniferous Plateau | 1 | 0 | 0 | 0 |
| 70g Northern Forested Plateau Escarpment | 0 | 0 | 0 | 0 |
| 70h Carter Hills | 0 | 0 | 0 | 0 |
| 71a Crawford-Mammoth Cave Uplands | 0 | 0 | 0 | 0 |
| 71b Mitchell Plain | 0 | 0 | 0 | 0 |
| 71c Knobs-Norman Upland | 0 | 0 | 0 | 0 |
| 71d Outer Bluegrass | 0 | 0 | 0 | 0 |
| 71e Western Pennyroyal Karst Plain | 0 | 0 | 0 | 0 |
| 71f Western Highland Rim | 0 | 0 | 0 | 0 |
| 71g Eastern Highland Rim | 1 | 0 | 0 | 1 |
| 71h Outer Nashville Basin | 1 | 0 | 0 | 0 |
| 71i Inner Nashville Basin | 0 | 0 | 0 | 0 |
| 71j Little Mountain | 0 | 0 | 0 | 0 |
| 71k Hills of the Bluegrass | 0 | 0 | 0 | 0 |
| 71l Inner Bluegrass | 0 | 0 | 0 | 0 |
| 72a Wabash-Ohio Bottomlands | 0 | 0 | 0 | 0 |
| 72b Glaciated Wabash Lowlands | 0 | 0 | 0 | 0 |
| 72c Green River-Southern Wabash Lowlands | 0 | 0 | 0 | 0 |
| 72h Caseyville Hills | 0 | 0 | 0 | 0 |
| 72j Southern Illinoian Till Plain | 0 | 0 | 0 | 0 |
| 72m Wabash River Bluffs and Low Hills | 0 | 0 | 0 | 0 |
| 75j Sea Islands/Coastal Marsh | 0 | 0 | 0 | 0 |
| 83a Erie/Ontario Lake Plain | 0 | 0 | 0 | 0 |
| 84a Cape Cod/Long Island | 0 | 0 | 0 | 0 |
| 84b Pine Barrens | 0 | 1 | 0 | 0 |
| 84c Barrier Islands/Coastal Marshes | 0 | 1 | 0 | 0 |
| 84d Inner Coastal Plain | 0 | 1 | 0 | 0 |
Phylogenetic Diversity Calculations
Match tree and dataframe
matched <- match.phylo.comm(tree, rasterstack_df.info3)Make object for tree including matching taxa only (non-matching taxa will be pruned out)
matchtree <- matched$phyMake object for dataset including matching taxa only (non-matching taxa removed)
matchcomm <- matched$comm Calculate phylogenetic distance matrix for use in MPD and MNTD calculations
MPD stands for mean pairwise distance, or mean phylogenetic distance among all pairs of species within a community. MNTD stands for mean nearest taxon distance, or the mean distance between each species within a community and its closest relative.
phydist <- cophenetic(matched$phy)Calculate indices using picante
Null model options include taxa.labels (shuffle tips of phylogeny) among others. Use ses.pd, ses.mpd and ses.mntd for more info on alternative null models and other parameters and output format. Number of runs - only using 99 runs (comparing to null model) due to time constraints.
PD calculates Faith’s PD and standard effect size of PD
PD stands for phylogenetic diversity
pd_result <-ses.pd(matchcomm, matchtree, null.model="taxa.labels", runs=99)Calculates mpd and ses mpd - equivalent to -NRI
NRI stands for net-relatedness index, standardizes MPD or mean pairwise distance. ses stands for standardized effect size. If we had abundance data, we could include that for mpd and mntd. The default is abundance.weighted = FALSE.
mpd_result <- ses.mpd(matchcomm, phydist, null.model="taxa.labels", abundance.weighted = FALSE, runs=99)Calculates mntd and ses mntd - equivalent to -NTI
NTI stands for nearest taxon index, standardizes MNTD mean nearest taxon distance. ses stands for standardized effect size.
mntd_result <- ses.mntd(matchcomm, phydist, null.model="taxa.labels", runs=99)Look at results
Positive ses values (obs.z values) and p values > 0.95 indicate overdispersion (greater than expected).Negative ses values (obs.z values) and p values < 0.05 indicate clustering (less than expected). Values not significantly different from zero indicate taxa in community randomly distributed across tree.
kable(pd_result) %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", font_size = 10)) %>%
scroll_box(width = "100%", height = "200px")| ntaxa | pd.obs | pd.rand.mean | pd.rand.sd | pd.obs.rank | pd.obs.z | pd.obs.p | runs | |
|---|---|---|---|---|---|---|---|---|
| 45a Southern Inner Piedmont | 2 | 1.50 | 1.318182 | 0.1883677 | 77.5 | 0.9652304 | 0.775 | 99 |
| 45b Southern Outer Piedmont | 3 | 2.00 | 1.810606 | 0.2232954 | 73.0 | 0.8481765 | 0.730 | 99 |
| 45c Carolina Slate Belt | 3 | 2.00 | 1.810606 | 0.2232954 | 73.0 | 0.8481765 | 0.730 | 99 |
| 45d Talladega Upland | 1 | 0.75 | 0.750000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 45e Northern Inner Piedmont | 2 | 1.50 | 1.318182 | 0.1883677 | 77.5 | 0.9652304 | 0.775 | 99 |
| 45f Northern Outer Piedmont | 2 | 1.25 | 1.338384 | 0.1648791 | 33.0 | -0.5360524 | 0.330 | 99 |
| 45g Triassic Basins | 3 | 2.00 | 1.810606 | 0.2232954 | 73.0 | 0.8481765 | 0.730 | 99 |
| 45i Kings Mountain | 2 | 1.50 | 1.318182 | 0.1883677 | 77.5 | 0.9652304 | 0.775 | 99 |
| 54a Illinois/Indiana Prairies | 0 | 0.00 | 0.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 54b Chicago Lake Plain | 0 | 0.00 | 0.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 54c Kankakee Marsh | 0 | 0.00 | 0.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 54d Sand Area | 0 | 0.00 | 0.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 54f Valparaiso-Wheaton Morainal Complex | 0 | 0.00 | 0.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 55a Clayey High Lime Till Plains | 0 | 0.00 | 0.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 55b Loamy High Lime Till Plains | 0 | 0.00 | 0.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 55c Mad River Interlobate Area | 0 | 0.00 | 0.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 55d Pre-Wisconsinan Drift Plains | 0 | 0.00 | 0.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 55e Darby Plains | 0 | 0.00 | 0.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 55f Whitewater Interlobate Area | 0 | 0.00 | 0.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 56a Northern Indiana Lake Country | 0 | 0.00 | 0.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 56b Battle Creek/Elkhart Outwash Plain | 0 | 0.00 | 0.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 56c Middle Tippecanoe Plains | 0 | 0.00 | 0.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 56d Michigan Lake Plain | 0 | 0.00 | 0.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 57a Maumee Lake Plain | 0 | 0.00 | 0.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 57b Oak Openings | 0 | 0.00 | 0.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 57c Paulding Plains | 0 | 0.00 | 0.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 57d Marblehead Drift/Limestone Plain | 0 | 0.00 | 0.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 58b Western New England Marble Valleys | 0 | 0.00 | 0.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 58e Berkshire Transition | 0 | 0.00 | 0.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 58h Reading Prong | 0 | 0.00 | 0.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 58i Glaciated Reading Prong/Hudson Highlands | 0 | 0.00 | 0.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 58x Taconic Foothills | 0 | 0.00 | 0.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 59a Connecticut Valley | 1 | 0.75 | 0.750000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 59c Southern New England Coastal Plains and Hills | 2 | 1.25 | 1.325758 | 0.2003013 | 35.5 | -0.3782182 | 0.355 | 99 |
| 59g Long Island Sound Coastal Lowland | 2 | 1.25 | 1.325758 | 0.2003013 | 35.5 | -0.3782182 | 0.355 | 99 |
| 59i Hudson Valley | 0 | 0.00 | 0.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 60a Glaciated Low Allegheny Plateau | 0 | 0.00 | 0.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 60b Delaware-Neversink Highlands | 0 | 0.00 | 0.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 61b Mosquito Creek/Pymatuning Lowlands | 0 | 0.00 | 0.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 61c Low Lime Drift Plain | 0 | 0.00 | 0.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 61d Erie Gorges | 0 | 0.00 | 0.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 61e Summit Interlobate Area | 0 | 0.00 | 0.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 62a Pocono High Plateau | 0 | 0.00 | 0.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 62b Low Poconos | 0 | 0.00 | 0.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 62c Glaciated High Allegheny Plateau | 0 | 0.00 | 0.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 62d Unglaciated High Allegheny Plateau | 0 | 0.00 | 0.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 63a Delaware River Terraces and Uplands | 2 | 1.25 | 1.325758 | 0.2003013 | 35.5 | -0.3782182 | 0.355 | 99 |
| 63b Chesapeake-Pamlico Lowlands and Tidal Marshes | 3 | 2.00 | 1.803030 | 0.2028896 | 77.5 | 0.9708220 | 0.775 | 99 |
| 63c Swamps and Peatlands | 2 | 1.50 | 1.348485 | 0.1849510 | 73.0 | 0.8192179 | 0.730 | 99 |
| 63d Virginian Barrier Islands and Coastal Marshes | 2 | 1.50 | 1.348485 | 0.1849510 | 73.0 | 0.8192179 | 0.730 | 99 |
| 63e Mid-Atlantic Flatwoods | 2 | 1.50 | 1.348485 | 0.1849510 | 73.0 | 0.8192179 | 0.730 | 99 |
| 63f Delmarva Uplands | 1 | 0.75 | 0.750000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 63g Carolinian Barrier Islands and Coastal Marshes | 3 | 1.75 | 1.820707 | 0.1989783 | 36.5 | -0.3553507 | 0.365 | 99 |
| 63h Carolina Flatwoods | 3 | 1.75 | 1.820707 | 0.1989783 | 36.5 | -0.3553507 | 0.365 | 99 |
| 63n Mid-Atlantic Floodplains and Low Terraces | 2 | 1.50 | 1.348485 | 0.1849510 | 73.0 | 0.8192179 | 0.730 | 99 |
| 64a Triassic Lowlands | 2 | 1.25 | 1.325758 | 0.2003013 | 35.5 | -0.3782182 | 0.355 | 99 |
| 64b Trap Rock and Conglomerate Uplands | 2 | 1.25 | 1.325758 | 0.2003013 | 35.5 | -0.3782182 | 0.355 | 99 |
| 64c Piedmont Uplands | 1 | 0.75 | 0.750000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 64d Piedmont Limestone/Dolomite Lowlands | 1 | 0.75 | 0.750000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 64e Glaciated Triassic Lowlands | 1 | 0.75 | 0.750000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 64f Passaic Basin Freshwater Wetlands | 1 | 0.75 | 0.750000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 64g Hackensack Meadowlands | 1 | 0.75 | 0.750000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 65c Sand Hills | 4 | 2.25 | 2.250000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 65i Fall Line Hills | 0 | 0.00 | 0.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 65j Transition Hills | 0 | 0.00 | 0.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 65l Atlantic Southern Loam Plains | 3 | 1.75 | 1.820707 | 0.1989783 | 36.5 | -0.3553507 | 0.365 | 99 |
| 65m Rolling Coastal Plain | 4 | 2.25 | 2.250000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 65n Chesapeake Rolling Coastal Plain | 2 | 1.25 | 1.325758 | 0.2003013 | 35.5 | -0.3782182 | 0.355 | 99 |
| 65p Southeastern Floodplains and Low Terraces | 2 | 1.50 | 1.328283 | 0.1877854 | 76.0 | 0.9144331 | 0.760 | 99 |
| 66a Northern Igneous Ridges | 2 | 1.50 | 1.318182 | 0.1883677 | 77.5 | 0.9652304 | 0.775 | 99 |
| 66b Northern Sedimentary and Metasedimentary Ridges | 2 | 1.50 | 1.318182 | 0.1883677 | 77.5 | 0.9652304 | 0.775 | 99 |
| 66c New River Plateau | 2 | 1.50 | 1.318182 | 0.1883677 | 77.5 | 0.9652304 | 0.775 | 99 |
| 66d Southern Crystalline Ridges and Mountains | 2 | 1.50 | 1.318182 | 0.1883677 | 77.5 | 0.9652304 | 0.775 | 99 |
| 66e Southern Sedimentary Ridges | 2 | 1.50 | 1.318182 | 0.1883677 | 77.5 | 0.9652304 | 0.775 | 99 |
| 66f Limestone Valleys and Coves | 2 | 1.50 | 1.318182 | 0.1883677 | 77.5 | 0.9652304 | 0.775 | 99 |
| 66g Southern Metasedimentary Mountains | 2 | 1.50 | 1.318182 | 0.1883677 | 77.5 | 0.9652304 | 0.775 | 99 |
| 66i High Mountains | 2 | 1.50 | 1.318182 | 0.1883677 | 77.5 | 0.9652304 | 0.775 | 99 |
| 66j Broad Basins | 2 | 1.50 | 1.318182 | 0.1883677 | 77.5 | 0.9652304 | 0.775 | 99 |
| 66k Amphibolite Mountains | 2 | 1.50 | 1.318182 | 0.1883677 | 77.5 | 0.9652304 | 0.775 | 99 |
| 66l Eastern Blue Ridge Foothills | 2 | 1.50 | 1.318182 | 0.1883677 | 77.5 | 0.9652304 | 0.775 | 99 |
| 66m Sauratown Mountains | 2 | 1.50 | 1.318182 | 0.1883677 | 77.5 | 0.9652304 | 0.775 | 99 |
| 67a Northern Limestone/Dolomite Valleys | 2 | 1.50 | 1.318182 | 0.1883677 | 77.5 | 0.9652304 | 0.775 | 99 |
| 67b Northern Shale Valleys | 2 | 1.50 | 1.318182 | 0.1883677 | 77.5 | 0.9652304 | 0.775 | 99 |
| 67c Northern Sandstone Ridges | 2 | 1.50 | 1.318182 | 0.1883677 | 77.5 | 0.9652304 | 0.775 | 99 |
| 67d Northern Dissected Ridges and Knobs | 2 | 1.50 | 1.318182 | 0.1883677 | 77.5 | 0.9652304 | 0.775 | 99 |
| 67e Anthracite Subregion | 0 | 0.00 | 0.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 67f Southern Limestone/Dolomite Valleys and Low Rolling Hills | 2 | 1.50 | 1.318182 | 0.1883677 | 77.5 | 0.9652304 | 0.775 | 99 |
| 67g Southern Shale Valleys | 2 | 1.50 | 1.318182 | 0.1883677 | 77.5 | 0.9652304 | 0.775 | 99 |
| 67h Southern Sandstone Ridges | 2 | 1.50 | 1.318182 | 0.1883677 | 77.5 | 0.9652304 | 0.775 | 99 |
| 67i Southern Dissected Ridges and Knobs | 2 | 1.50 | 1.318182 | 0.1883677 | 77.5 | 0.9652304 | 0.775 | 99 |
| 67j Northern Glaciated Limestone Valleys | 0 | 0.00 | 0.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 67k Northern Glaciated Shale and Slate Valleys | 0 | 0.00 | 0.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 67l Northern Glaciated Limestone Ridges, Valleys, and Terraces | 0 | 0.00 | 0.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 67m Northern Glaciated Ridges | 0 | 0.00 | 0.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 68a Cumberland Plateau | 2 | 1.50 | 1.318182 | 0.1883677 | 77.5 | 0.9652304 | 0.775 | 99 |
| 68b Sequatchie Valley | 1 | 0.75 | 0.750000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 68c Plateau Escarpment | 2 | 1.50 | 1.318182 | 0.1883677 | 77.5 | 0.9652304 | 0.775 | 99 |
| 68d Southern Table Plateaus | 1 | 0.75 | 0.750000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 68e Dissected Plateau | 0 | 0.00 | 0.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 68f Shale Hills | 0 | 0.00 | 0.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 69a Forested Hills and Mountains | 2 | 1.50 | 1.318182 | 0.1883677 | 77.5 | 0.9652304 | 0.775 | 99 |
| 69b Uplands and Valleys of Mixed Land Use | 0 | 0.00 | 0.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 69c Greenbriar Karst | 2 | 1.50 | 1.318182 | 0.1883677 | 77.5 | 0.9652304 | 0.775 | 99 |
| 69d Dissected Appalachian Plateau | 2 | 1.50 | 1.318182 | 0.1883677 | 77.5 | 0.9652304 | 0.775 | 99 |
| 69e Cumberland Mountain Thrust Block | 2 | 1.50 | 1.318182 | 0.1883677 | 77.5 | 0.9652304 | 0.775 | 99 |
| 70a Permian Hills | 0 | 0.00 | 0.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 70b Monongahela Transition Zone | 1 | 0.75 | 0.750000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 70c Pittsburgh Low Plateau | 0 | 0.00 | 0.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 70d Knobs-Lower Scioto Dissected Plateau | 0 | 0.00 | 0.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 70e Unglaciated Upper Muskingum Basin | 0 | 0.00 | 0.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 70f Ohio/Kentucky Carboniferous Plateau | 1 | 0.75 | 0.750000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 70g Northern Forested Plateau Escarpment | 0 | 0.00 | 0.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 70h Carter Hills | 0 | 0.00 | 0.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 71a Crawford-Mammoth Cave Uplands | 0 | 0.00 | 0.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 71b Mitchell Plain | 0 | 0.00 | 0.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 71c Knobs-Norman Upland | 0 | 0.00 | 0.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 71d Outer Bluegrass | 0 | 0.00 | 0.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 71e Western Pennyroyal Karst Plain | 0 | 0.00 | 0.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 71f Western Highland Rim | 0 | 0.00 | 0.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 71g Eastern Highland Rim | 2 | 1.50 | 1.318182 | 0.1883677 | 77.5 | 0.9652304 | 0.775 | 99 |
| 71h Outer Nashville Basin | 1 | 0.75 | 0.750000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 71i Inner Nashville Basin | 0 | 0.00 | 0.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 71j Little Mountain | 0 | 0.00 | 0.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 71k Hills of the Bluegrass | 0 | 0.00 | 0.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 71l Inner Bluegrass | 0 | 0.00 | 0.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 72a Wabash-Ohio Bottomlands | 0 | 0.00 | 0.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 72b Glaciated Wabash Lowlands | 0 | 0.00 | 0.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 72c Green River-Southern Wabash Lowlands | 0 | 0.00 | 0.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 72h Caseyville Hills | 0 | 0.00 | 0.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 72j Southern Illinoian Till Plain | 0 | 0.00 | 0.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 72m Wabash River Bluffs and Low Hills | 0 | 0.00 | 0.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 75j Sea Islands/Coastal Marsh | 0 | 0.00 | 0.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 83a Erie/Ontario Lake Plain | 0 | 0.00 | 0.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 84a Cape Cod/Long Island | 0 | 0.00 | 0.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 84b Pine Barrens | 1 | 0.75 | 0.750000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 84c Barrier Islands/Coastal Marshes | 1 | 0.75 | 0.750000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 84d Inner Coastal Plain | 1 | 0.75 | 0.750000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
kable(mpd_result) %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", font_size = 10)) %>%
scroll_box(width = "100%", height = "200px")| ntaxa | mpd.obs | mpd.rand.mean | mpd.rand.sd | mpd.obs.rank | mpd.obs.z | mpd.obs.p | runs | |
|---|---|---|---|---|---|---|---|---|
| 45a Southern Inner Piedmont | 2 | 1.500000 | 1.171717 | 0.3790537 | 74.5 | 0.8660589 | 0.745 | 99 |
| 45b Southern Outer Piedmont | 3 | 1.333333 | 1.193603 | 0.1900398 | 73.0 | 0.7352705 | 0.730 | 99 |
| 45c Carolina Slate Belt | 3 | 1.333333 | 1.193603 | 0.1900398 | 73.0 | 0.7352705 | 0.730 | 99 |
| 45d Talladega Upland | 1 | NA | NaN | NA | NA | NA | NA | 99 |
| 45e Northern Inner Piedmont | 2 | 1.500000 | 1.171717 | 0.3790537 | 74.5 | 0.8660589 | 0.745 | 99 |
| 45f Northern Outer Piedmont | 2 | 1.000000 | 1.202020 | 0.3705979 | 30.5 | -0.5451196 | 0.305 | 99 |
| 45g Triassic Basins | 3 | 1.333333 | 1.193603 | 0.1900398 | 73.0 | 0.7352705 | 0.730 | 99 |
| 45i Kings Mountain | 2 | 1.500000 | 1.171717 | 0.3790537 | 74.5 | 0.8660589 | 0.745 | 99 |
| 54a Illinois/Indiana Prairies | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 54b Chicago Lake Plain | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 54c Kankakee Marsh | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 54d Sand Area | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 54f Valparaiso-Wheaton Morainal Complex | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 55a Clayey High Lime Till Plains | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 55b Loamy High Lime Till Plains | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 55c Mad River Interlobate Area | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 55d Pre-Wisconsinan Drift Plains | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 55e Darby Plains | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 55f Whitewater Interlobate Area | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 56a Northern Indiana Lake Country | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 56b Battle Creek/Elkhart Outwash Plain | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 56c Middle Tippecanoe Plains | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 56d Michigan Lake Plain | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 57a Maumee Lake Plain | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 57b Oak Openings | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 57c Paulding Plains | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 57d Marblehead Drift/Limestone Plain | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 58b Western New England Marble Valleys | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 58e Berkshire Transition | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 58h Reading Prong | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 58i Glaciated Reading Prong/Hudson Highlands | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 58x Taconic Foothills | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 59a Connecticut Valley | 1 | NA | NaN | NA | NA | NA | NA | 99 |
| 59c Southern New England Coastal Plains and Hills | 2 | 1.000000 | 1.131313 | 0.3818982 | 37.5 | -0.3438433 | 0.375 | 99 |
| 59g Long Island Sound Coastal Lowland | 2 | 1.000000 | 1.131313 | 0.3818982 | 37.5 | -0.3438433 | 0.375 | 99 |
| 59i Hudson Valley | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 60a Glaciated Low Allegheny Plateau | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 60b Delaware-Neversink Highlands | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 61b Mosquito Creek/Pymatuning Lowlands | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 61c Low Lime Drift Plain | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 61d Erie Gorges | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 61e Summit Interlobate Area | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 62a Pocono High Plateau | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 62b Low Poconos | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 62c Glaciated High Allegheny Plateau | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 62d Unglaciated High Allegheny Plateau | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 63a Delaware River Terraces and Uplands | 2 | 1.000000 | 1.131313 | 0.3818982 | 37.5 | -0.3438433 | 0.375 | 99 |
| 63b Chesapeake-Pamlico Lowlands and Tidal Marshes | 3 | 1.333333 | 1.144781 | 0.2124878 | 77.5 | 0.8873554 | 0.775 | 99 |
| 63c Swamps and Peatlands | 2 | 1.500000 | 1.131313 | 0.3751589 | 78.0 | 0.9827486 | 0.780 | 99 |
| 63d Virginian Barrier Islands and Coastal Marshes | 2 | 1.500000 | 1.131313 | 0.3751589 | 78.0 | 0.9827486 | 0.780 | 99 |
| 63e Mid-Atlantic Flatwoods | 2 | 1.500000 | 1.131313 | 0.3751589 | 78.0 | 0.9827486 | 0.780 | 99 |
| 63f Delmarva Uplands | 1 | NA | NaN | NA | NA | NA | NA | 99 |
| 63g Carolinian Barrier Islands and Coastal Marshes | 3 | 1.166667 | 1.164983 | 0.2095981 | 38.0 | 0.0080320 | 0.380 | 99 |
| 63h Carolina Flatwoods | 3 | 1.166667 | 1.164983 | 0.2095981 | 38.0 | 0.0080320 | 0.380 | 99 |
| 63n Mid-Atlantic Floodplains and Low Terraces | 2 | 1.500000 | 1.131313 | 0.3751589 | 78.0 | 0.9827486 | 0.780 | 99 |
| 64a Triassic Lowlands | 2 | 1.000000 | 1.131313 | 0.3818982 | 37.5 | -0.3438433 | 0.375 | 99 |
| 64b Trap Rock and Conglomerate Uplands | 2 | 1.000000 | 1.131313 | 0.3818982 | 37.5 | -0.3438433 | 0.375 | 99 |
| 64c Piedmont Uplands | 1 | NA | NaN | NA | NA | NA | NA | 99 |
| 64d Piedmont Limestone/Dolomite Lowlands | 1 | NA | NaN | NA | NA | NA | NA | 99 |
| 64e Glaciated Triassic Lowlands | 1 | NA | NaN | NA | NA | NA | NA | 99 |
| 64f Passaic Basin Freshwater Wetlands | 1 | NA | NaN | NA | NA | NA | NA | 99 |
| 64g Hackensack Meadowlands | 1 | NA | NaN | NA | NA | NA | NA | 99 |
| 65c Sand Hills | 4 | 1.166667 | 1.166667 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 65i Fall Line Hills | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 65j Transition Hills | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 65l Atlantic Southern Loam Plains | 3 | 1.166667 | 1.164983 | 0.2095981 | 38.0 | 0.0080320 | 0.380 | 99 |
| 65m Rolling Coastal Plain | 4 | 1.166667 | 1.166667 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 65n Chesapeake Rolling Coastal Plain | 2 | 1.000000 | 1.131313 | 0.3818982 | 37.5 | -0.3438433 | 0.375 | 99 |
| 65p Southeastern Floodplains and Low Terraces | 2 | 1.500000 | 1.207071 | 0.3572150 | 73.0 | 0.8200364 | 0.730 | 99 |
| 66a Northern Igneous Ridges | 2 | 1.500000 | 1.171717 | 0.3790537 | 74.5 | 0.8660589 | 0.745 | 99 |
| 66b Northern Sedimentary and Metasedimentary Ridges | 2 | 1.500000 | 1.171717 | 0.3790537 | 74.5 | 0.8660589 | 0.745 | 99 |
| 66c New River Plateau | 2 | 1.500000 | 1.171717 | 0.3790537 | 74.5 | 0.8660589 | 0.745 | 99 |
| 66d Southern Crystalline Ridges and Mountains | 2 | 1.500000 | 1.171717 | 0.3790537 | 74.5 | 0.8660589 | 0.745 | 99 |
| 66e Southern Sedimentary Ridges | 2 | 1.500000 | 1.171717 | 0.3790537 | 74.5 | 0.8660589 | 0.745 | 99 |
| 66f Limestone Valleys and Coves | 2 | 1.500000 | 1.171717 | 0.3790537 | 74.5 | 0.8660589 | 0.745 | 99 |
| 66g Southern Metasedimentary Mountains | 2 | 1.500000 | 1.171717 | 0.3790537 | 74.5 | 0.8660589 | 0.745 | 99 |
| 66i High Mountains | 2 | 1.500000 | 1.171717 | 0.3790537 | 74.5 | 0.8660589 | 0.745 | 99 |
| 66j Broad Basins | 2 | 1.500000 | 1.171717 | 0.3790537 | 74.5 | 0.8660589 | 0.745 | 99 |
| 66k Amphibolite Mountains | 2 | 1.500000 | 1.171717 | 0.3790537 | 74.5 | 0.8660589 | 0.745 | 99 |
| 66l Eastern Blue Ridge Foothills | 2 | 1.500000 | 1.171717 | 0.3790537 | 74.5 | 0.8660589 | 0.745 | 99 |
| 66m Sauratown Mountains | 2 | 1.500000 | 1.171717 | 0.3790537 | 74.5 | 0.8660589 | 0.745 | 99 |
| 67a Northern Limestone/Dolomite Valleys | 2 | 1.500000 | 1.171717 | 0.3790537 | 74.5 | 0.8660589 | 0.745 | 99 |
| 67b Northern Shale Valleys | 2 | 1.500000 | 1.171717 | 0.3790537 | 74.5 | 0.8660589 | 0.745 | 99 |
| 67c Northern Sandstone Ridges | 2 | 1.500000 | 1.171717 | 0.3790537 | 74.5 | 0.8660589 | 0.745 | 99 |
| 67d Northern Dissected Ridges and Knobs | 2 | 1.500000 | 1.171717 | 0.3790537 | 74.5 | 0.8660589 | 0.745 | 99 |
| 67e Anthracite Subregion | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 67f Southern Limestone/Dolomite Valleys and Low Rolling Hills | 2 | 1.500000 | 1.171717 | 0.3790537 | 74.5 | 0.8660589 | 0.745 | 99 |
| 67g Southern Shale Valleys | 2 | 1.500000 | 1.171717 | 0.3790537 | 74.5 | 0.8660589 | 0.745 | 99 |
| 67h Southern Sandstone Ridges | 2 | 1.500000 | 1.171717 | 0.3790537 | 74.5 | 0.8660589 | 0.745 | 99 |
| 67i Southern Dissected Ridges and Knobs | 2 | 1.500000 | 1.171717 | 0.3790537 | 74.5 | 0.8660589 | 0.745 | 99 |
| 67j Northern Glaciated Limestone Valleys | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 67k Northern Glaciated Shale and Slate Valleys | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 67l Northern Glaciated Limestone Ridges, Valleys, and Terraces | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 67m Northern Glaciated Ridges | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 68a Cumberland Plateau | 2 | 1.500000 | 1.171717 | 0.3790537 | 74.5 | 0.8660589 | 0.745 | 99 |
| 68b Sequatchie Valley | 1 | NA | NaN | NA | NA | NA | NA | 99 |
| 68c Plateau Escarpment | 2 | 1.500000 | 1.171717 | 0.3790537 | 74.5 | 0.8660589 | 0.745 | 99 |
| 68d Southern Table Plateaus | 1 | NA | NaN | NA | NA | NA | NA | 99 |
| 68e Dissected Plateau | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 68f Shale Hills | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 69a Forested Hills and Mountains | 2 | 1.500000 | 1.171717 | 0.3790537 | 74.5 | 0.8660589 | 0.745 | 99 |
| 69b Uplands and Valleys of Mixed Land Use | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 69c Greenbriar Karst | 2 | 1.500000 | 1.171717 | 0.3790537 | 74.5 | 0.8660589 | 0.745 | 99 |
| 69d Dissected Appalachian Plateau | 2 | 1.500000 | 1.171717 | 0.3790537 | 74.5 | 0.8660589 | 0.745 | 99 |
| 69e Cumberland Mountain Thrust Block | 2 | 1.500000 | 1.171717 | 0.3790537 | 74.5 | 0.8660589 | 0.745 | 99 |
| 70a Permian Hills | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 70b Monongahela Transition Zone | 1 | NA | NaN | NA | NA | NA | NA | 99 |
| 70c Pittsburgh Low Plateau | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 70d Knobs-Lower Scioto Dissected Plateau | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 70e Unglaciated Upper Muskingum Basin | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 70f Ohio/Kentucky Carboniferous Plateau | 1 | NA | NaN | NA | NA | NA | NA | 99 |
| 70g Northern Forested Plateau Escarpment | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 70h Carter Hills | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 71a Crawford-Mammoth Cave Uplands | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 71b Mitchell Plain | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 71c Knobs-Norman Upland | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 71d Outer Bluegrass | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 71e Western Pennyroyal Karst Plain | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 71f Western Highland Rim | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 71g Eastern Highland Rim | 2 | 1.500000 | 1.171717 | 0.3790537 | 74.5 | 0.8660589 | 0.745 | 99 |
| 71h Outer Nashville Basin | 1 | NA | NaN | NA | NA | NA | NA | 99 |
| 71i Inner Nashville Basin | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 71j Little Mountain | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 71k Hills of the Bluegrass | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 71l Inner Bluegrass | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 72a Wabash-Ohio Bottomlands | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 72b Glaciated Wabash Lowlands | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 72c Green River-Southern Wabash Lowlands | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 72h Caseyville Hills | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 72j Southern Illinoian Till Plain | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 72m Wabash River Bluffs and Low Hills | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 75j Sea Islands/Coastal Marsh | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 83a Erie/Ontario Lake Plain | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 84a Cape Cod/Long Island | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 84b Pine Barrens | 1 | NA | NaN | NA | NA | NA | NA | 99 |
| 84c Barrier Islands/Coastal Marshes | 1 | NA | NaN | NA | NA | NA | NA | 99 |
| 84d Inner Coastal Plain | 1 | NA | NaN | NA | NA | NA | NA | 99 |
kable(mntd_result) %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", font_size = 10)) %>%
scroll_box(width = "100%", height = "200px")| ntaxa | mntd.obs | mntd.rand.mean | mntd.rand.sd | mntd.obs.rank | mntd.obs.z | mntd.obs.p | runs | |
|---|---|---|---|---|---|---|---|---|
| 45a Southern Inner Piedmont | 2 | 1.5000000 | 1.1767677 | 0.3522025 | 76.0 | 0.9177457 | 0.760 | 99 |
| 45b Southern Outer Piedmont | 3 | 1.1666667 | 0.9747475 | 0.2107696 | 74.0 | 0.9105639 | 0.740 | 99 |
| 45c Carolina Slate Belt | 3 | 1.1666667 | 0.9747475 | 0.2107696 | 74.0 | 0.9105639 | 0.740 | 99 |
| 45d Talladega Upland | 1 | NA | NaN | NA | NA | NA | NA | 99 |
| 45e Northern Inner Piedmont | 2 | 1.5000000 | 1.1767677 | 0.3522025 | 76.0 | 0.9177457 | 0.760 | 99 |
| 45f Northern Outer Piedmont | 2 | 1.0000000 | 1.2020202 | 0.3971788 | 30.5 | -0.5086380 | 0.305 | 99 |
| 45g Triassic Basins | 3 | 1.1666667 | 0.9747475 | 0.2107696 | 74.0 | 0.9105639 | 0.740 | 99 |
| 45i Kings Mountain | 2 | 1.5000000 | 1.1767677 | 0.3522025 | 76.0 | 0.9177457 | 0.760 | 99 |
| 54a Illinois/Indiana Prairies | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 54b Chicago Lake Plain | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 54c Kankakee Marsh | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 54d Sand Area | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 54f Valparaiso-Wheaton Morainal Complex | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 55a Clayey High Lime Till Plains | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 55b Loamy High Lime Till Plains | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 55c Mad River Interlobate Area | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 55d Pre-Wisconsinan Drift Plains | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 55e Darby Plains | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 55f Whitewater Interlobate Area | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 56a Northern Indiana Lake Country | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 56b Battle Creek/Elkhart Outwash Plain | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 56c Middle Tippecanoe Plains | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 56d Michigan Lake Plain | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 57a Maumee Lake Plain | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 57b Oak Openings | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 57c Paulding Plains | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 57d Marblehead Drift/Limestone Plain | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 58b Western New England Marble Valleys | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 58e Berkshire Transition | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 58h Reading Prong | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 58i Glaciated Reading Prong/Hudson Highlands | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 58x Taconic Foothills | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 59a Connecticut Valley | 1 | NA | NaN | NA | NA | NA | NA | 99 |
| 59c Southern New England Coastal Plains and Hills | 2 | 1.0000000 | 1.1464646 | 0.3863257 | 36.0 | -0.3791222 | 0.360 | 99 |
| 59g Long Island Sound Coastal Lowland | 2 | 1.0000000 | 1.1464646 | 0.3863257 | 36.0 | -0.3791222 | 0.360 | 99 |
| 59i Hudson Valley | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 60a Glaciated Low Allegheny Plateau | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 60b Delaware-Neversink Highlands | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 61b Mosquito Creek/Pymatuning Lowlands | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 61c Low Lime Drift Plain | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 61d Erie Gorges | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 61e Summit Interlobate Area | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 62a Pocono High Plateau | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 62b Low Poconos | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 62c Glaciated High Allegheny Plateau | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 62d Unglaciated High Allegheny Plateau | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 63a Delaware River Terraces and Uplands | 2 | 1.0000000 | 1.1464646 | 0.3863257 | 36.0 | -0.3791222 | 0.360 | 99 |
| 63b Chesapeake-Pamlico Lowlands and Tidal Marshes | 3 | 1.1666667 | 0.9461279 | 0.2269140 | 75.5 | 0.9719045 | 0.755 | 99 |
| 63c Swamps and Peatlands | 2 | 1.5000000 | 1.1111111 | 0.3680863 | 80.0 | 1.0565155 | 0.800 | 99 |
| 63d Virginian Barrier Islands and Coastal Marshes | 2 | 1.5000000 | 1.1111111 | 0.3680863 | 80.0 | 1.0565155 | 0.800 | 99 |
| 63e Mid-Atlantic Flatwoods | 2 | 1.5000000 | 1.1111111 | 0.3680863 | 80.0 | 1.0565155 | 0.800 | 99 |
| 63f Delmarva Uplands | 1 | NA | NaN | NA | NA | NA | NA | 99 |
| 63g Carolinian Barrier Islands and Coastal Marshes | 3 | 0.8333333 | 0.9579125 | 0.2237885 | 39.0 | -0.5566823 | 0.390 | 99 |
| 63h Carolina Flatwoods | 3 | 0.8333333 | 0.9579125 | 0.2237885 | 39.0 | -0.5566823 | 0.390 | 99 |
| 63n Mid-Atlantic Floodplains and Low Terraces | 2 | 1.5000000 | 1.1111111 | 0.3680863 | 80.0 | 1.0565155 | 0.800 | 99 |
| 64a Triassic Lowlands | 2 | 1.0000000 | 1.1464646 | 0.3863257 | 36.0 | -0.3791222 | 0.360 | 99 |
| 64b Trap Rock and Conglomerate Uplands | 2 | 1.0000000 | 1.1464646 | 0.3863257 | 36.0 | -0.3791222 | 0.360 | 99 |
| 64c Piedmont Uplands | 1 | NA | NaN | NA | NA | NA | NA | 99 |
| 64d Piedmont Limestone/Dolomite Lowlands | 1 | NA | NaN | NA | NA | NA | NA | 99 |
| 64e Glaciated Triassic Lowlands | 1 | NA | NaN | NA | NA | NA | NA | 99 |
| 64f Passaic Basin Freshwater Wetlands | 1 | NA | NaN | NA | NA | NA | NA | 99 |
| 64g Hackensack Meadowlands | 1 | NA | NaN | NA | NA | NA | NA | 99 |
| 65c Sand Hills | 4 | 0.8750000 | 0.8750000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 65i Fall Line Hills | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 65j Transition Hills | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 65l Atlantic Southern Loam Plains | 3 | 0.8333333 | 0.9579125 | 0.2237885 | 39.0 | -0.5566823 | 0.390 | 99 |
| 65m Rolling Coastal Plain | 4 | 0.8750000 | 0.8750000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 65n Chesapeake Rolling Coastal Plain | 2 | 1.0000000 | 1.1464646 | 0.3863257 | 36.0 | -0.3791222 | 0.360 | 99 |
| 65p Southeastern Floodplains and Low Terraces | 2 | 1.5000000 | 1.1818182 | 0.3673856 | 74.5 | 0.8660705 | 0.745 | 99 |
| 66a Northern Igneous Ridges | 2 | 1.5000000 | 1.1767677 | 0.3522025 | 76.0 | 0.9177457 | 0.760 | 99 |
| 66b Northern Sedimentary and Metasedimentary Ridges | 2 | 1.5000000 | 1.1767677 | 0.3522025 | 76.0 | 0.9177457 | 0.760 | 99 |
| 66c New River Plateau | 2 | 1.5000000 | 1.1767677 | 0.3522025 | 76.0 | 0.9177457 | 0.760 | 99 |
| 66d Southern Crystalline Ridges and Mountains | 2 | 1.5000000 | 1.1767677 | 0.3522025 | 76.0 | 0.9177457 | 0.760 | 99 |
| 66e Southern Sedimentary Ridges | 2 | 1.5000000 | 1.1767677 | 0.3522025 | 76.0 | 0.9177457 | 0.760 | 99 |
| 66f Limestone Valleys and Coves | 2 | 1.5000000 | 1.1767677 | 0.3522025 | 76.0 | 0.9177457 | 0.760 | 99 |
| 66g Southern Metasedimentary Mountains | 2 | 1.5000000 | 1.1767677 | 0.3522025 | 76.0 | 0.9177457 | 0.760 | 99 |
| 66i High Mountains | 2 | 1.5000000 | 1.1767677 | 0.3522025 | 76.0 | 0.9177457 | 0.760 | 99 |
| 66j Broad Basins | 2 | 1.5000000 | 1.1767677 | 0.3522025 | 76.0 | 0.9177457 | 0.760 | 99 |
| 66k Amphibolite Mountains | 2 | 1.5000000 | 1.1767677 | 0.3522025 | 76.0 | 0.9177457 | 0.760 | 99 |
| 66l Eastern Blue Ridge Foothills | 2 | 1.5000000 | 1.1767677 | 0.3522025 | 76.0 | 0.9177457 | 0.760 | 99 |
| 66m Sauratown Mountains | 2 | 1.5000000 | 1.1767677 | 0.3522025 | 76.0 | 0.9177457 | 0.760 | 99 |
| 67a Northern Limestone/Dolomite Valleys | 2 | 1.5000000 | 1.1767677 | 0.3522025 | 76.0 | 0.9177457 | 0.760 | 99 |
| 67b Northern Shale Valleys | 2 | 1.5000000 | 1.1767677 | 0.3522025 | 76.0 | 0.9177457 | 0.760 | 99 |
| 67c Northern Sandstone Ridges | 2 | 1.5000000 | 1.1767677 | 0.3522025 | 76.0 | 0.9177457 | 0.760 | 99 |
| 67d Northern Dissected Ridges and Knobs | 2 | 1.5000000 | 1.1767677 | 0.3522025 | 76.0 | 0.9177457 | 0.760 | 99 |
| 67e Anthracite Subregion | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 67f Southern Limestone/Dolomite Valleys and Low Rolling Hills | 2 | 1.5000000 | 1.1767677 | 0.3522025 | 76.0 | 0.9177457 | 0.760 | 99 |
| 67g Southern Shale Valleys | 2 | 1.5000000 | 1.1767677 | 0.3522025 | 76.0 | 0.9177457 | 0.760 | 99 |
| 67h Southern Sandstone Ridges | 2 | 1.5000000 | 1.1767677 | 0.3522025 | 76.0 | 0.9177457 | 0.760 | 99 |
| 67i Southern Dissected Ridges and Knobs | 2 | 1.5000000 | 1.1767677 | 0.3522025 | 76.0 | 0.9177457 | 0.760 | 99 |
| 67j Northern Glaciated Limestone Valleys | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 67k Northern Glaciated Shale and Slate Valleys | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 67l Northern Glaciated Limestone Ridges, Valleys, and Terraces | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 67m Northern Glaciated Ridges | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 68a Cumberland Plateau | 2 | 1.5000000 | 1.1767677 | 0.3522025 | 76.0 | 0.9177457 | 0.760 | 99 |
| 68b Sequatchie Valley | 1 | NA | NaN | NA | NA | NA | NA | 99 |
| 68c Plateau Escarpment | 2 | 1.5000000 | 1.1767677 | 0.3522025 | 76.0 | 0.9177457 | 0.760 | 99 |
| 68d Southern Table Plateaus | 1 | NA | NaN | NA | NA | NA | NA | 99 |
| 68e Dissected Plateau | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 68f Shale Hills | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 69a Forested Hills and Mountains | 2 | 1.5000000 | 1.1767677 | 0.3522025 | 76.0 | 0.9177457 | 0.760 | 99 |
| 69b Uplands and Valleys of Mixed Land Use | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 69c Greenbriar Karst | 2 | 1.5000000 | 1.1767677 | 0.3522025 | 76.0 | 0.9177457 | 0.760 | 99 |
| 69d Dissected Appalachian Plateau | 2 | 1.5000000 | 1.1767677 | 0.3522025 | 76.0 | 0.9177457 | 0.760 | 99 |
| 69e Cumberland Mountain Thrust Block | 2 | 1.5000000 | 1.1767677 | 0.3522025 | 76.0 | 0.9177457 | 0.760 | 99 |
| 70a Permian Hills | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 70b Monongahela Transition Zone | 1 | NA | NaN | NA | NA | NA | NA | 99 |
| 70c Pittsburgh Low Plateau | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 70d Knobs-Lower Scioto Dissected Plateau | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 70e Unglaciated Upper Muskingum Basin | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 70f Ohio/Kentucky Carboniferous Plateau | 1 | NA | NaN | NA | NA | NA | NA | 99 |
| 70g Northern Forested Plateau Escarpment | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 70h Carter Hills | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 71a Crawford-Mammoth Cave Uplands | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 71b Mitchell Plain | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 71c Knobs-Norman Upland | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 71d Outer Bluegrass | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 71e Western Pennyroyal Karst Plain | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 71f Western Highland Rim | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 71g Eastern Highland Rim | 2 | 1.5000000 | 1.1767677 | 0.3522025 | 76.0 | 0.9177457 | 0.760 | 99 |
| 71h Outer Nashville Basin | 1 | NA | NaN | NA | NA | NA | NA | 99 |
| 71i Inner Nashville Basin | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 71j Little Mountain | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 71k Hills of the Bluegrass | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 71l Inner Bluegrass | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 72a Wabash-Ohio Bottomlands | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 72b Glaciated Wabash Lowlands | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 72c Green River-Southern Wabash Lowlands | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 72h Caseyville Hills | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 72j Southern Illinoian Till Plain | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 72m Wabash River Bluffs and Low Hills | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 75j Sea Islands/Coastal Marsh | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 83a Erie/Ontario Lake Plain | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 84a Cape Cod/Long Island | 0 | NA | NaN | NA | NA | NA | NA | 99 |
| 84b Pine Barrens | 1 | NA | NaN | NA | NA | NA | NA | 99 |
| 84c Barrier Islands/Coastal Marshes | 1 | NA | NaN | NA | NA | NA | NA | 99 |
| 84d Inner Coastal Plain | 1 | NA | NaN | NA | NA | NA | NA | 99 |
Plot PD results by community
ggplot() +
geom_point(data = pd_result, aes(x = rownames(pd_result), y = pd.obs, col = pd.obs)) +
ggtitle("PD values by ecoregion") +
xlab("Community") +
ylab("PD") +
theme(text = element_text(size=10), axis.text.x = element_text(angle = 90, hjust = 1))
Plot MPD results by community
ggplot()+
geom_point(data = mpd_result, aes(x = rownames(mpd_result), y = mpd.obs, col = mpd.obs))+
ggtitle("MPD values by ecoregion")+
xlab("Community")+
ylab("MPD")+
theme(text = element_text(size=10), axis.text.x = element_text(angle = 90, hjust = 1))## Warning: Removed 86 rows containing missing values (geom_point).
Plot MNTD results by community
ggplot()+
geom_point(data = mntd_result, aes(x = rownames(mntd_result), y = mntd.obs, col = mntd.obs))+
ggtitle("MNTD values by ecoregion")+
xlab("Community")+
ylab("MNTD")+
theme(text = element_text(size=10), axis.text.x = element_text(angle = 90, hjust = 1))## Warning: Removed 86 rows containing missing values (geom_point).